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Selection: with tag prediction-bias [27 articles] 


Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Ecography, Vol. 40, No. 8. (1 August 2017), pp. 913-929,


Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also ...


Stacked generalization

Neural Networks, Vol. 5, No. 2. (January 1992), pp. 241-259,


This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When ...


Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics

Global Ecology and Biogeography, Vol. 27, No. 2. (February 2018), pp. 245-256,


[Aim] Species distribution modelling, a family of statistical methods that predicts species distributions from a set of occurrences and environmental predictors, is now routinely applied in many macroecological studies. However, the reliability of evaluation metrics usually employed to validate these models remains questioned. Moreover, the emergence of online databases of environmental variables with global coverage, especially climatic, has favoured the use of the same set of standard predictors. Unfortunately, the selection of variables is too rarely based on a careful examination of ...


On the projection of future fire danger conditions with various instantaneous/mean-daily data sources

Climatic Change, Vol. 118, No. 3-4. (2013), pp. 827-840,


Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean ...


Climate impacts from a removal of anthropogenic aerosol emissions

Geophys. Res. Lett. (24 January 2018), 2017GL076079,


Limiting global warming to 1.5 or 2.0°C requires strong mitigation of anthropogenic greenhouse gas (GHG) emissions. Concurrently, emissions of anthropogenic aerosols will decline, due to coemission with GHG, and measures to improve air quality. However, the combined climate effect of GHG and aerosol emissions over the industrial era is poorly constrained. Here we show the climate impacts from removing present-day anthropogenic aerosol emissions and compare them to the impacts from moderate GHG-dominated global warming. Removing aerosols induces a global mean surface ...


Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

Ecological Applications, Vol. 19, No. 1. (January 2009), pp. 181-197,


Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with ...


Spatiotemporal patterns of changes in fire regime and climate: defining the pyroclimates of south-eastern France (Mediterranean Basin)

Climatic Change, Vol. 129, No. 1-2. (2015), pp. 239-251,


The impacts of climate change on fires are expected to be highly variable spatially and temporally. In heavily anthropized landscapes, the great number of factors affecting fire regimes further limits our ability to predict future fire activity caused by climate. To address this, we develop a new framework for analysing regional changes in fire regimes from specific spatiotemporal patterns of fires and climate, so-called pyroclimates. We aim to test the trends of fire activity and climate (1973–2009) across the Mediterranean and ...


Effects of incorporating spatial autocorrelation into the analysis of species distribution data

Global Ecology and Biogeography, Vol. 16, No. 2. (March 2007), pp. 129-138,


[Aim]  Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed ‘spatial models’). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models. [Methods]  I review ecological studies that compare spatial and non-spatial models. [Results]  In all ...


Comparison between the USLE, the USLE-M and replicate plots to model rainfall erosion on bare fallow areas

CATENA, Vol. 145 (October 2016), pp. 39-46,


[Highlights] [::] Examines ability of soil losses from a plot to predict those from another [::] Stochastic and systemic variations observed when replicate model used [::] Replicate model tends to perform better that USLE-M when runoff known. [Abstract] It has been proposed that the best physical model of erosion from a plot is provided by a replicate plot (Nearing, 1998). Event data from paired bare fallow plots in the USLE database were used to examine the abilities of replicate plots, the USLE and the USLE-M to ...


(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 28

(February 2014)
Keywords: inrmm-list-of-tags   power-law   ppm   practice   pre-alpine   pre-print   precaution   precaution-principle   precipitation   precisely-wrong   precursor-research   predation   predator-satiation   predatory-publishers   prediction   prediction-bias   predictive-modelling   predictors   predisposition   premature-optimization   preparedness   preprints   prescribed-burn   presence-absence   presence-only   pressure-volume-curves   pressures   prestoea-montana   pretreatment   prey-predator   pricing   primary-productivity   principal-components-regression   prisoners-dilemma   pristiphora-abietina   probability-vs-possibility   problem-driven   processes   processing   production-rules   productivity   programming   progressive-learning   prolog   proportion   prosopis-alba   prosopis-glandulosa   prosopis-pallida   protected-areas   protected-species   protection   protective-forest   protocol-uncertainty   provenance   provisioning-services   pruning   prunus-avium   prunus-cerasifera   prunus-domestica   prunus-dulcis   prunus-fruticosa   prunus-ilicifolia   prunus-laurocerasus   prunus-mahaleb   prunus-malaheb   prunus-padus   prunus-salicina   prunus-serotina   prunus-spinosa   prunus-spp   prunus-tenella   pseudo-absences   pseudo-random   pseudoaraucaria-spp   pseudolarix-spp   pseudomonas-avellanae   pseudomonas-spp   pseudomonas-syringae   pseudotsuga   pseudotsuga-macrocarpa   pseudotsuga-menziesii   pseudotsuga-spp   psychology   pterocarpus-indicus   pterocarpus-officinalis   pterocarya-pterocarpa   public-domain   publication-bias   publication-delay   publication-errors   publish-or-perish   puccinia-coronata   pull-push-pest-control   pulp   punica-granatum   purdiaea-nutans   pyrenees-region   pyrolysis   pyrus-amygdaliformis   pyrus-browiczii  


List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( ). ...


Climate science: where climate models fall short

Nature, Vol. 531, No. 7592. (2 March 2016), pp. 10-10,


Climate models tend to overestimate the extent to which climate change contributes to weather events such as extreme heat and rain. [\n] Omar Bellprat and Francisco Doblas-Reyes at the Catalan Institute of Climate Sciences in Barcelona, Spain, used an idealized statistical model to compare the frequency of weather extremes in simulations [...] ...


Economics: current climate models are grossly misleading

Nature, Vol. 530, No. 7591. (24 February 2016), pp. 407-409,


Nicholas Stern calls on scientists, engineers and economists to help policymakers by better modelling the immense risks to future generations, and the potential for action. [Excerpt] The twin defining challenges of our century are overcoming poverty and managing climate change. If we can tackle these issues together, we will create a secure and prosperous world for generations to come. If we don't, the future is at grave risk. [\n] Researchers across a range of disciplines must work together to help decision-makers in the ...


Accurately measuring model prediction error



When assessing the quality of a model, being able to accurately measure its prediction error is of key importance. Often, however, techniques of measuring error are used that give grossly misleading results. This can lead to the phenomenon of over-fitting where a model may fit the training data very well, but will do a poor job of predicting results for new data not used in model training. Here is an overview of methods to accurately measure model prediction error. ...

Visual summary


Understanding the bias-variance tradeoff



When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". There is a tradeoff between a model's ability to minimize bias and variance. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. ...

Visual summary


Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies

International Journal of Wildland Fire, Vol. 19, No. 4. (2010), 377,


To control and use wildland fires safely and effectively depends on creditable assessments of fire potential, including the propensity for crowning in conifer forests. Simulation studies that use certain fire modelling systems (i.e. NEXUS, FlamMap, FARSITE, FFE-FVS (Fire and Fuels Extension to the Forest Vegetation Simulator), Fuel Management Analyst (FMAPlus®), BehavePlus) based on separate implementations or direct integration of Rothermel’s surface and crown rate of fire spread models with Van Wagner’s crown fire transition and propagation models are shown to have ...


Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Ecography, Vol. 36, No. 1. (1 January 2013), pp. 27-46,


Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or ...

Visual summary


Increasing drought under global warming in observations and models

Nature Climate Change, Vol. 3, No. 1. (5 August 2012), pp. 52-58,


Historical records of precipitation, streamflow and drought indices all show increased aridity since 1950 over many land areas1, 2. Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century. There are, however, large differences in the observed and model-simulated drying patterns1, 2, 6. Reconciling these differences is necessary before the model predictions can be trusted. Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large ...


Evaluating the utility of dynamical downscaling in agricultural impacts projections

Proceedings of the National Academy of Sciences, Vol. 111, No. 24. (17 June 2014), pp. 8776-8781,


Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations ...


Pruning of memories by context-based prediction error

Proceedings of the National Academy of Sciences, Vol. 111, No. 24. (17 June 2014), pp. 8997-9002,


[Significance] Forgetting is often considered to be bad, but selective forgetting of unreliable information can have the positive side effect of reducing mental clutter, thereby making it easier to access our most important memories. Prior studies of forgetting have focused on passive mechanisms (decay, interference) or on effortful inhibition by cognitive control. Here we report the discovery of an active mechanism for forgetting that weakens memories selectively and without burdening the conscious mind. Specifically, we show that the brain automatically generates ...


Why soil erosion models over-predict small soil losses and under-predict large soil losses

CATENA, Vol. 32, No. 1. (February 1998), pp. 15-22,


Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless of whether the model is empirical or physically based. The hypothesis presented herein is that this phenomenon is not necessarily ...


Predictive ecology: systems approaches

Philosophical transactions of the Royal Society of London. Series B, Biological sciences, Vol. 367, No. 1586. (19 January 2012), pp. 163-169,


The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive--to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the ...


State-dependent climate sensitivity in past warm climates and its implications for future climate projections

Proceedings of the National Academy of Sciences, Vol. 110, No. 35. (27 August 2013), pp. 14162-14167,


Projections of future climate depend critically on refined estimates of climate sensitivity. Recent progress in temperature proxies dramatically increases the magnitude of warming reconstructed from early Paleogene greenhouse climates and demands a close examination of the forcing and feedback mechanisms that maintained this warmth and the broad dynamic range that these paleoclimate records attest to. Here, we show that several complementary resolutions to these questions are possible in the context of model simulations using modern and early Paleogene configurations. We find ...


Overestimated global warming over the past 20 years

Nature Clim. Change, Vol. 3, No. 9. (28 September 2013), pp. 767-769,


Recent observed global warming is significantly less than that simulated by climate models. This difference might be explained by some combination of errors in external forcing, model response and internal climate variability. ...


What are climate models missing?

Science, Vol. 340, No. 6136. (31 May 2013), pp. 1053-1054,


Fifty years ago, Joseph Smagorinsky published a landmark paper (1) describing numerical experiments using the primitive equations (a set of fluid equations that describe global atmospheric flows). In so doing, he introduced what later became known as a General Circulation Model (GCM). GCMs have come to provide a compelling framework for coupling the atmospheric circulation to a great variety of processes. Although early GCMs could only consider a small subset of these processes, it was widely appreciated that a more comprehensive ...


Missing feedbacks, asymmetric uncertainties, and the underestimation of future warming

Geophysical Research Letters, Vol. 33, No. 10. (26 May 2006), pp. n/a-n/a,


Historical evidence shows that atmospheric greenhouse gas (GhG) concentrations increase during periods of warming, implying a positive feedback to future climate change. We quantified this feedback for CO2 and CH4 by combining the mathematics of feedback with empirical ice-core information and general circulation model (GCM) climate ...


Systematic model error: the impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations

Journal of Climate In Journal of Climate, Vol. 25, No. 14. (16 April 2012), pp. 4946-4962,


AbstractLong-standing systematic model errors in both tropics and extratropics of the ECMWF model run at a horizontal resolution typical for climate models are investigated. Based on the hypothesis that the misrepresentation of unresolved scales contributes to the systematic model error, three model refinements aimed at their representation?fluctuating or deterministically?are investigated.Increasing horizontal resolution to explicitly simulate smaller-scale features, representing subgrid-scale fluctuations by a stochastic parameterization, and improving the deterministic physics parameterizations all lead to a decrease in the systematic bias of the ...


The upper end of climate model temperature projections is inconsistent with past warming

Environmental Research Letters, Vol. 8, No. 1. (01 March 2013), 014024,


Climate models predict a large range of possible future temperatures for a particular scenario of future emissions of greenhouse gases and other anthropogenic forcings of climate. Given that further warming in coming decades could threaten increasing risks of climatic disruption, it is important to determine whether model projections are consistent with temperature changes already observed. This can be achieved by quantifying the extent to which increases in well mixed greenhouse gases and changes in other anthropogenic and natural forcings have already ...

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Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
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INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.