Bush, bugs, and birds; interdependency in a farming landscape
Bibliographic record
Abstract
Changes in farming practices over the second half of the twentieth century greatly reduced the extent of natural areas remaining within agricultural landscapes. Field margins and hedgerows have recently been recognized as important habitat in maintaining wildlife diversity and proper ecosystem functioning. Ecotones, defined as the transitionary area of vegetation between woody plant species and the arable crop, are an especially important landscape element for birds and arthropods. In this manuscript, we aimed to evaluate which hedgerow attribute was best at predicting avian densities in a conventional and organic farming landscape. Furthermore, we wished to investigate if these same hedgerow attributes could explain arthropod family density, richness and diversity, and how these were correlated to avian densities. An information theory-based multimodel inference method was used to identify which factors influenced variability in avian densities. Although not always significant, avian densities increased with arthropod richness at our study sites. Ecotone width is the best predictor of avian densities and arthropod richness while percent gap is the most important factor if a manager wishes to increase avian diversity (H’) in hedgerow habitats. Increasing ecotone width benefits both avian densities and arthropod richness that in turn further increases bird numbers in our farming landscape.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".