Impacts of sea otter (<i>Enhydra lutris</i>) predation on commercially important sea cucumbers (<i>Parastichopus californicus</i>) in southeast Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it