A null‐model for significance testing of presence‐only species distribution models
Bibliographic record
Abstract
Species' distribution models (SDMs) attempt to predict the potential distribution of species by interpolating identified relationships between species' presence/ absence, or presence-only data on one hand, and environmental predictors on the other hand, to a geographical area of interest. Currently, they are widely applied in biogeography, conservation biology, ecology, palaeo-ecology, invasive species studies, and wildlife management (Guisan and Zimmermann 2000, Araijo and Pearson 2005, Thuiller et al. 2005, Peterson 2006, Aratijo and Guisan 2006, Guisan et al. 2006). More recently, vast numbers of herbarium and natural history museum collections have become available (Graham et al. 2004) and techniques to apply this special type of presence-only data have been developed (Hirzel et al. 2002, Anderson et al. 2003, Pearce and Boyce 2006, Elith et al. 2006, Phillips et al. 2006). Despite the widespread use of SDMs, several high-priority research interests remain to be investigated (Guisan and Thuiller 2005, Aradjo and Guisan 2006). One of these is the improvement of SDM validation, or the quantification of a model's predictive performance (Araijo and Guisan 2006). The fact that the standard validation procedures for an SDM are not sufficient to assess the applicability of an SDM in a predictive context, was first shown by Olden et al. (2002). They showed that after SDM validation it is critical to assess whether the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.090 | 0.193 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".