Flight initiation by Ferruginous Hawks depends on disturbance type, experience, and the anthropogenic landscape
Bibliographic record
Abstract
The expansion of humans and their related infrastructure is increasing the likelihood that wildlife will interact with humans. When disturbed by humans, animals often change their behaviour, which can result in time and energetic costs to that animal. An animal's decision to change behaviour is likely related to the type of disturbance, the individual's past experience with disturbance, and the landscape in which the disturbance occurs. In southern Alberta and Saskatchewan, we quantified probability of flight initiation from the nest by Ferruginous Hawks (Buteo regalis) during approaches to nests by investigators. We tested if probability of flight was related to different disturbance types, previous experience, and the anthropogenic landscape in which individual Ferruginous Hawks nested. Probability of flight was related to the type of approach by the investigator, the number of previous visits by investigators, and the vehicular traffic around the nest. Approaches by humans on foot resulted in a greater probability of flight than those in a vehicle. Approaches in a vehicle via low traffic volume access roads were related to increased probability of flight relative to other road types. The number of previous investigator approaches to the nest increased the probability of flight. Overall, we found support that Ferruginous Hawks show habituation to vehicles and the positive reinforcement hypotheses as probability of flight was negatively related to an index of traffic activity near the nest. Our work emphasizes that complex, dynamic processes drive the decision to initiate flight from the nest, and contributes to the growing body of work explaining how responses to humans vary within species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".