Data from: Farmland heterogeneity benefits bats in agricultural landscapes.
Bibliographic record
Abstract
Abstract from associated article: Pressure to increase food production poses a challenge for biodiversity conservation in agricultural landscapes. Previous studies suggest that one potential way to enhance biodiversity without taking land out of production is to increase the landscape heterogeneity of farmland by increasing the diversity of crop types in the landscape, and/or the complexity of the spatial pattern of the crop fields (e.g., by decreasing field sizes). Thus we hypothesize that farmland heterogeneity should also increase bat abundance and richness in agricultural landscapes. Here, we use data on bat activity and richness collected using acoustic surveys in rural eastern Ontario, Canada to test the predictions that there should be greater bat activity and greater species richness in agricultural landscapes with higher Shannon diversity of crops and smaller fields, when controlling for the effect of total crop cover. Bat activity increased with farmland heterogeneity, as predicted. Farmland heterogeneity was also positively related to species richness, although the relationship was not statistically supported. Positive effects of farmland heterogeneity on bats will be of interest to farmers and agricultural policy-makers, given the potential economic benefits of pest control by bats.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.097 | 0.007 |
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".