Shrub density effects on the presence of an endangered lizard of the Carrizo Plain National Monument, California
Bibliographic record
Abstract
Abstract Positive associations between animals and foundational shrub species are frequent in desert ecosystems for shelter, resources, refuge, and other key ecological processes. Herein, we tested the impact of the density of the shrub species Ephedra californica on the presence and habitat use of the federally endangered lizard species, Gambelia sila. To do this, we used a 3‐year radio telemetry dataset and satellite‐based counts of shrub density across sites at the Carrizo Plain National Monument in San Luis Obispo County, CA. The effect of shrub density on lizard presence was contrasted with previous shrub cover analyses to determine whether measures of shrub density were superior to shrub cover in predicting lizard presence. Increasing shrub density increased lizard presence. As shrub density increased, lizards were located more frequently “above ground” versus “below ground” in burrows. Male lizards had significantly larger home ranges than females, but both sexes were similarly associated with increasing shrub densities. Shrub density and shrub cover models did not significantly differ in their prediction of lizard presence. These findings suggest that both habitat measures are effective analogs and that ecologically, both cover and the density of foundation shrub species are key factors for some desert lizards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".