Shrub and vegetation cover predict resource selection use by an endangered species of desert lizard
Bibliographic record
Abstract
Globally, no species is exempt from the constraints associated with limited available habitat or resources, and endangered species in particular warrant critical examination. In most cases, these species are restricted to limited locations, and the relative likelihood of resource use within the space they can access is important. Using Gambelia sila, one of the first vertebrate species listed as endangered, we used resource selection function analysis of telemetry and remotely sensed data to identity key drivers of selected versus available locations for this species in Carrizo Plain National Monument, USA. We examined the probability of selection given different resource types. Increasing shrub cover, lower and relatively more flat sites, increasing normalized difference vegetation index, and solar radiation all significantly predicted likelihood of observed selection within the area sampled. Imagery data were also validated with fine-scale field data showing that large-scale contrasts of selection relative to available location patterns for animal species are a useful lens for potential habitat. Key environmental infrastructure such as foundation plant species including shrubs or local differences in the physical attributes were relevant to this endangered 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".