Assessing methods to live‐capture wild boars ( <i>Sus scrofa</i> ) in urban and peri‐urban environments
Bibliographic record
Abstract
BACKGROUND: ) populations are increasing worldwide and invading urban areas. Live-capture can improve the management of this challenge, maximising efficiency, allowing scientific studies and potentially improving animal welfare. This study assesses teleanaesthesia, drop-net, corral trap and cage trap to live-capture wild boar in urban and peri-urban areas, evaluating efficiency and animal stress through haematology and serum biochemistry. METHODS: From 2012 to 2018, 655 wild boars were captured in 279 operations (drop-net=17, teleanaesthesia=186, cage trap=66 and corral trap=10) in the urban and peri-urban areas of Barcelona (Spain). Haematological and serum biochemical variables were determined in 145 wild boars (42 drop-netted, 41 teleanaesthetised, 38 cage-trapped and 24 corral-trapped). RESULTS: Performance (wild boars captured per operation) was highest for drop-net, followed by corral and cage traps, and finally teleanaesthesia. The three physical capture methods were more stressful than teleanaesthesia, causing a more intense physiological reaction, muscular damage, renal function impairment and homeostasis adaption. Stress response was predominantly adrenergic for drop-net and cortisol-induced for cage and corral traps. CONCLUSION: Teleanaesthesia is the choice in reactive urban situations thanks to its adaptability; drop-net effectively targets wild boars in peri-urban environments; cage and corral traps are useful as long-term methods in specific areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".