Effect of spatial resolution, algorithm and variable set on the estimated distribution of a mammal of concern: the squirrel<i>Sciurus aberti</i>
Bibliographic record
Abstract
Most potential habitat models have been built from WorldClim using low resolution variables, even for areas of high heterogeneity with few weather stations. The resulting models can be too general and lead to erroneous decisions when used for conservation purposes. Sciurus aberti is a tree squirrel inhabiting highlands in the SW US and the Sierra Madre Occidental (SMO) in Mexico, where it is considered a species of low concern. We examined the effect of resolution, variables, and algorithms on the predicted potential habitat of S. aberti in Mexico and compared the resulting models against a previous one created from WorldClim variables using GARP (Genetic Algorithm for Rule Set Production). Our best model, using Maxent, 30 m spatial resolution and topographic variables, predicted a fragmented distribution in pine and pine–oak forests, consistent with what is known about the species’ natural history. The area represented only 2% of the SMO (compared to 28% for the GARP model), of which only 0.33% lies within protected areas. The model suggests that the habitat is highly fragmented, which threatens population continuity. Therefore, we propose that the conservation status of Sciurus aberti must be reassessed and that forest management better consider the conservation of arboreal species.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".