Wild boar effects on hair‐tube sampling
Bibliographic record
Abstract
Abstract Hair tubes are one of the most effective tools for sampling small‐mammal assemblages. Despite their efficiency, they can be damaged by wildlife. We quantified wildlife‐induced disturbance of hair‐tube sampling in the Monte Pisano mountain system in Italy. At each site we tied 2 tubes together to form a hair trap and placed them in 3 different areas. We measured disturbances by counting the number of hair traps disturbed and identified the species that caused damage with cameras. Although approximately 27% (n = 164/600) of hair traps were disturbed, 2 hair traps/site allowed us to collect data from 97% of site checks from undisturbed and retrieved hair traps. Wild boars (Sus scrofa) were attracted to hair traps by olfactory and acoustic signals and caused the most disturbances. Displaced tubes detected a similar number of hairs and number of species as undisturbed tubes; species richness estimates that include retrieved displaced tubes should provide reliable data. To avoid data loss when conducting hair‐trap monitoring, we suggest using ≥2 hair traps per site.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".