Examining the utility of fishery and survey data to detect prey removal effects on Steller sea lions (<i>Eumetopias jubatus</i>)
Bibliographic record
Abstract
One focus of mitigation for Steller sea lion (Eumetopias jubatus) declines in Alaska has been to restrict commercial fishery activity around sea lion rookeries and haul-outs. However, a variety of statistical hypothesis tests have failed to relate sea lion population metrics to fish and fishing variables, prompting speculation that regulations may be unwarranted. In this study, we use simulation to show that standard hypothesis tests often have overstated power to detect a relationship between Steller sea lion vital rates and fish or fishing variables. The power and utility of hypothesis tests largely depend on choosing appropriate dependent and independent variables. In particular, pup counts were the most effective for diagnosing fecundity effects, and successive ratios of adult counts were the most effective for diagnosing survival effects. Fish relative abundance was the most effective independent variable, with other choices (e.g., fishery catch) often resulting in misleading inferences. We argue that Bayes factors are best suited for characterizing the relationship between fish abundance and Steller sea lion vital rates and that existing evidence does not preclude a strong relationship between sea lion fecundity and the availability of commercially harvested fish stocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".