Food supplementation and abundance estimation in the white-footed mouse
Bibliographic record
Abstract
Food availability often drives consumer population dynamics. However, food availability may also influence capture probability, which if not accounted for may create bias in estimating consumer abundance and confound the effects of food availability on consumer population dynamics. This study compared two commonly used abundance indices (minimum number alive (MNA) and number of animals captured per night per grid) with an abundance estimator based on robust design model as applied to the white-footed mouse ( Peromyscus leucopus (Rafinesque, 1818)) in food supplementation experiments. MNA consistently generated abundance estimates similar to the robust design model, regardless of food supplementation. The number of animals captured per night per grid, however, consistently generated lower abundance estimates compared with MNA and the robust design model. Nevertheless, the correlations between abundance estimates from MNA, number of animals captured, and robust design model were not influenced by food supplementation. This study demonstrated that food supplementation is not likely to create bias among these different measures of abundance. Therefore, there is a great potential for conducting meta-analysis of food supplementation effect on consumer population dynamics (particularly in small mammals) across studies using different abundance indices and estimators.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".