Species of Concern within the Salish Sea nearly double between 2002 and 2013
Bibliographic record
Abstract
Species of concern are native species, sub-species or ecologically significant units that warrant special attention to ensure their conservation. The number of species of concern within an ecosystem can be used as a crude measure of ecosystem health. Within the Salish Sea, four jurisdictions assess which species require special initiatives to ensure protection and survival of the population: the Province of British Columbia, the State of Washington, the Canadian Federal Government, and the United States Federal Government. Also known as marine species at risk, the number of species of concern in the Salish Sea is used by the US Environmental Protection Agency and Environment Canada as a transboundary ecosystem indicator. As of November 15, 2013, there were 119 species at risk in the Salish Sea, almost twice the number of species at risk when the indicator was first established in 2002. While some of this increase represents an increase in the number of fish, bird and mammal species known to use the Salish Sea, most additions represent new listings due to concern about declines in populations. In terms of species richness, currently 35% of mammal species, 32% of bird species, 17% of fish species, 100% of reptile species, and less than 1% of macro invertebrate species are listed by one or more jurisdiction. The high proportion of species of concern is suggestive of ecosystem decay and we recommend that it is time to consider the Salish Sea an ecosystem of concern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".