Spatial Habitat Use Patterns of Sea Otters in Coastal Washington
Bibliographic record
Abstract
Sea otter (Enhydra lutris kenyoni) movements, home range, and activity budgets were described from data collected during very-high-frequency radiotelemetry studies of 75 individuals on the outer coast of Washington State between 1992 and 1999. Sea otters were located at least once per week from 22 accessible sites along the coast. Over the 7-year study period, range expansion occurred from the core range north and east into the Strait of Juan de Fuca (SJF) as well as southward on the outer coast. Forty-three percent of the sea otters moved into the SJF at least once, most often in winter, using habitat that had not been occupied by sea otters since their extirpation 100 years ago. All sea otters spent portions of their time in the vicinity of Cape Alava, and many animals demonstrated consistent periodic seasonal shifts between specific portions of the coastline over several years. Ninety-five percent annual linear home ranges differed between sex and age classes. Adult males used the largest amount of coastline (50 km ± 9 SD) and subadult females used the least (24 ± 9 km). Both adult males and females demonstrated high seasonal periodicity in range use in summer and winter. Twenty-four-hour time budgets in the core portion of the range revealed on average sea otters spent 41% ± 14% SD of the time foraging and 45% ± 13% of the time resting (age and sex classes pooled). Adult and subadult female sea otters were most frequently found resting and foraging close to shore (< 1,000 m) and in shallow water (0–10 m), whereas adult and subadult males rested and foraged > 1,000 m offshore and at depths between 10 and 30 m. Given current rates of population growth and observed mobility, sea otters in Washington have high potential for range expansion into unoccupied habitat such as Grays Harbor, Willapa Bay, the SJF, or along Vancouver Island.
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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 | 0.001 | 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".