Raptors, racing pigeons and perceptions of attacks
Bibliographic record
Abstract
Abstract Raptors are often the cause of human-wildlife conflict because they may predate economically valuable species, and it is the perceived extent of predation that may augment conflict between raptors and people who keep and race pigeons. This study uses data obtained through questionnaires and an online raptor-attack reporting feature to investigate the frequency of racing pigeon losses and the perceptions of pigeon fanciers. Responses suggest that those who kept more pigeons and entered more races lost a higher proportion of pigeons. Losses were also influenced by the predatory species: sparrowhawks (Accipter nisus) were more likely to attack pigeons at lofts, whilst peregrines (Falco peregrinus) were more likely to attack pigeons during training, with patterns linked to the raptors’ breeding seasons. Pigeon fanciers were almost unanimous in their perception that raptors threaten the future of the hobby. Previous studies show that only a small proportion of racing pigeons are likely to be lost to raptors, yet pigeon fanciers believe that raptors are the main cause of losses, highlighting a possible mismatch between the perceived and actual causes of loss. This misconception may be a main source of this human-raptor conflict, so educating pigeon fanciers about the true impact of raptors could help to alleviate the issue. A shift in the beginning of the pigeon racing season by one month, and careful sighting of pigeon lofts in gardens, are also recommended in order to reduce raptor attacks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".