The behavioural response of lactating Weddell seals (Leptonychotes weddellii) to over-snow vehicles: a case study
Bibliographic record
Abstract
Over-snow vehicles are used extensively at Antarctic scientific research stations. Adult female Weddell seals ( Leptonychotes weddellii (Lesson, 1826)) also utilise the fast-ice and are therefore often exposed to vehicular activity, with the potential of affecting their behaviour. Guidelines for vehicular travel have been developed to minimise disturbance to Antarctic wildlife; however, these guidelines have not yet been scientifically tested. To examine the efficiency and sensitivity of existing guidelines used within the Australian Antarctic Territory (AAT), we conducted drive-by experiments of two types of over-snow vehicles. The results of these experiments showed that the probability of a lactating Weddell seal looking at the vehicles and the duration of the seals’ looking at the vehicles were dependent on the distance between vehicles and seals, the position of the pups in relation to its mother, and the distance the adult female was from the water. Although the seals apparently perceived the vehicles to be a threat, no seals fled in response to vehicle activity. We propose that the existing guidelines used in the AAT could be amended to increase separation distances between vehicles and breeding Weddell seals if the goal of management is to ensure that Weddell seal behaviour is unaffected by vehicle travel.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".