Modelling the abundance of grey seals (Halichoerus grypus) along the Norwegian coast
Bibliographic record
Abstract
Abstract Øigård, T. A., Frie, A. K., Nilssen, K. T., and Hammill, M. O. 2012. Modelling the abundance of grey seals (Halichoerus grypus) along the Norwegian coast. – ICES Journal of Marine Science, 69: . An age-structured population dynamics model of the Norwegian grey seal (Halichoerus grypus) population has been developed. The model is of a Bayesian character in the sense that priors for various parameters were used. Model runs indicated an increase in the abundance of the total Norwegian grey seal population during the last 30 years, suggesting a total of 8740 (95% confidence interval: 7320–10 170) animals in 2011. A total catch of 707 (95% confidence interval: 532–882) grey seals would maintain the population size at the 2011 level. Model runs suggest that current catch levels will likely result in a reduction in the population size in Sør-Trøndelag and Nord-Trøndelag counties, and an increase in the population size in Rogaland, Nordland, Troms, and Finnmark counties. The model runs assumed that 80% of the seals taken in Rogaland came from the UK and that 50 and 55% of the catches in Troms and Finnmark, respectively, were immigrants from Russia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".