Use of Multiple Methods to Estimate Walrus (<i>Odobenus rosmarus rosmarus</i>) Abundance in the Penny Strait-Lancaster Sound and West Jones Sound Stocks, Canada
Bibliographic record
Abstract
Surveys to estimate walrus abundance at terrestrial haulout sites in the Penny Strait-Lancaster Sound (PS-LS) and West Jones Sound (WJS) stocks were conducted in 1977 and 1998-2009. The Minimum Counted Population (MCP) was similar in 1977 (565) to recent years (557) for the PS-LS stock. The MCP for the WJS stock was higher in recent surveys (404) than in 1977 (290). Regression analysis of MCP and density (number of walrus divided by number of haulouts surveyed) showed no significant trends over time. We also calculated bounded count estimates for comparison. Finally, we used broad-scale behavioural data to estimate the proportion of the total stock that could be considered countable, to produce two adjusted estimates. We selected recent surveys with good coverage and ignored adjusted estimates that were lower than MCP. For the PS-LS stock, the adjusted MCP (with 95% CL) was 672 (575-768) and 727 (623-831) walrus in 2007 and 2009, respectively. For WJS, the best estimates were the adjusted MCP of 503 (473-534) in 2008 and the adjusted bounded count of 470 (297-1732) in 2009. While both stocks appear to have remained stable over three decades, differences in survey coverage and possible differences in walrus distribution make precise population estimation difficult.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 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".