Estimating the Number of Walruses in Svalbard from Aerial Surveys and Behavioural Data from Satellite Telemetry
Bibliographic record
Abstract
All known terrestrial haul-out sites for walruses in Svalbard (n = 79) were surveyed during the period 1–3 August 2006, and sites that were in use (n = 17) were documented using digital photography. A total of 657 walruses were counted on land in the resultant images. An extensive behavioural data set from walruses equipped with satellite relay data loggers, covering August 2002 to August 2005, was used to account for walruses that were in the water. The proportion of walruses at sea during the survey was calculated to be 0.750 on the basis of 28 thirty-day periods from 23 male walruses. Time of day and wind chill did not significantly affect haul-out behaviour. However, a logistic regression model revealed both a correlation among haul-out patterns of individuals within years, and a year effect (chi-square = 6.42, df = 2, p = 0.04). Because the survey was not flown in a year when satellite tags were deployed, the interannual variance was retained in a model (with no other explanatory variables). The overdispersion parameter from this model was 2.02 (deviance = 28.33, df = 14). Thus, variance in proportions of time individuals spent at sea was multiplied by this parameter to achieve a corrected SE around the estimate. The 95% CI based on this SE corresponded to a proportion of walruses at sea during the survey between 0.717 and 0.781, resulting in an estimated total number of walruses in Svalbard in August 2006 of 2629 (95% CI: 2318– 2998).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".