Birds and Mammals that Depend on the Salish Sea: A Compilation
Bibliographic record
Abstract
Efforts are underway to restore the Salish Sea, a 16,925 km2 inland sea shared by Washington State, USA, and British Columbia, Canada. A list of the birds and mammals that use this marine ecosystem is lacking. We compiled information from varied sources and identified 172 bird and 37 mammal species that depend on the Salish Sea marine ecosystem. Of these species, 72 bird and 29 mammal species are both highly dependent on intertidal or marine habitat as well as on marine derived food. One hundred bird species and 8 mammal species that use the Salish Sea marine ecosystem have varying degrees of dependence on the marine and terrestrial ecosystems to meet significant life history needs. These interactions between the marine and terrestrial ecosystems indicate the need to integrate marine and terrestrial restoration efforts to achieve long-term conservation of the suite of birds and mammals that use and depend on the marine ecosystem. This comprehensive list of avian and mammal fauna for the Salish Sea serves as a foundation for determining the occurrence of new species and the disappearance of others, enables selection of species as indicators for ecosystem health, and also provides a basis for identifying the mechanisms responsible for marine bird and mammal declines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".