Evaluating the influence of environmental variables on the population trajectory of Atlantic walrus (Odobenus rosmarus rosmarus)
Bibliographic record
Abstract
Fisheries and Oceans Canada (DFO) is committed to an Ecosystem Approach to Fisheries Management (EAFM) of fisheries resources, including subsistence hunts of Arctic marine mammals. Here, we present a case study on the incorporation of environmental variables (EVs) into Atlantic walrus (Odobenus rosmarus rosmarus) stock assessment, a ‘data-poor’ species for which our understanding of how EVs affect demographics is limited. An existing Bayesian stock-production model for the Hudson Bay-Davis Strait (HBDS) management stock was modified to incorporate July sea ice concentration (SIC) into either the process error term or the annual maximum population growth term (λmax). Both models examined the influence of July SIC in year n on population growth in year n, with the assumption that sea ice extent affects foraging conditions during early lactation, an energetically demanding period for mothers when calf mortality is high. Both models suggested the effect of year n July SIC on year n population growth was non-existent or weakly negative. There was no evidence of lagged effects, with neither year n-1 or year n-2 July SIC strongly influencing year n population growth. Overall, the ‘data-poor’ nature of the HBDS stock, with a limited time series of abundance estimates, coupled with coarse-scale SIC measurements may have limited our ability to detect a strong effect. Directed research linking sea ice conditions and population growth is necessary to improve implementation of the EAFM stock assessment framework for walrus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".