Impacts of sea otter (<i>Enhydra lutris</i>) predation on commercially important sea cucumbers (<i>Parastichopus californicus</i>) in southeast Alaska
Bibliographic record
Abstract
Sea cucumbers (Parastichopus californicus), which are an important commercial, subsistence, and ecological resource, are negatively affected by an expanding sea otter (Enhydra lutris) population in southeast Alaska. A few hundred sea otters were reintroduced into southeast Alaska in the late 1960s after their extirpation during the 18th and 19th century fur trade. In the ensuing decades after recolonization, the sea otter population grew exponentially in number and distribution, and sea cucumbers declined in density in areas with otters, suggesting an inverse relationship between sea otter numbers and sea cucumber density. We evaluated the interaction and effects of sea otters on sea cucumbers using sea otter foraging observations, sea otter population survey data, and sea cucumber density data. Our results indicate that sea cucumber density declined with and without sea otter presence and that the extent of the decline depends on the duration and magnitude of sea otter presence, with 100% decline in areas occupied by sea otters since 1994. Sea otter predation should be included in sea cucumber fishery management as a step toward ecosystem-based management.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".