Animal burrow presence patterns and local shrub density in Central California Deserts
Bibliographic record
Abstract
Abstract Background Ecological resource availability is crucial for the survival of local desert animal communities. Landscape resources such as shrubs and burrows provide several mechanisms that can benefit associating animal species typically through reducing harsh abiotic factors. Since many of these shrubs act as foundational species within desert ecosystems, understanding how these resources, along with those created by local vertebrate species, can provide key insights into habitat utilization. Here, we test to see if there is an association between the presence of burrows created by local desert species and the total density of foundational shrubs, across various Central California deserts. This was tested through a combination of burrow field surveys and satellite imagery. All data was combined to determine if there is a relationship between both resources for desert vertebrate species. Results We found that there were significantly more burrows associated with foundational shrub species across Central California deserts and that shrub density positively predicts the presence of burrows. In several of the tested ecosystems, increasing shrub densities positively predicted higher probabilities of burrow presence. Conclusions The existence of two highly utilized desert resources, and the relationship between them, signals that areas abundant in both resources can positively impact local animal 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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".