Agricultural land use alters trophic status and population density of deer mice (<i>Peromyscus maniculatus</i>) on the North American Great Plains
Bibliographic record
Abstract
Habitat conversion is among the most important causes of environmental change worldwide, yet relatively little is known about its potential influence on trophic interactions. We investigated the effects of agricultural land use on carbon and nitrogen stable isotope values, trophic status, population density, and body condition of deer mice ( Peromyscus maniculatus (Wagner, 1845)) in a grassland ecosystem. Muscle δ15N (cropland = 7.6‰ ± 1.3‰; hay fields = 7.9‰ ± 1.3‰; native prairie = 7.2‰ ± 2.1‰) from deer mice did not vary with land use despite baseline soil and vegetation δ15N differences. Enrichment of deer mice over vegetation (Δδ15N) was, on average, a full trophic level (~2.5‰) higher on native prairie (6.4‰ ± 1.6‰) than on cropland (3.9‰ ± 2.3‰), and intermediate in hay fields (5.9‰ ± 2.0‰). Relative density of deer mice was more than twofold higher in crop and hay fields compared with native prairie, but body condition did not vary with land use. Our results suggest that agricultural activity caused a shift in the trophic level and relative abundance of a generalist grassland omnivore. Soil and vegetation δ15N reflected anthropogenic N inputs to agricultural fields but were not useful as general markers of habitat use in this study.
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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 | 0.000 |
| 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) | 0.001 | 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".