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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".