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Record W7002002852

Marine Species at Risk: A Salish Sea Transboundary Indicator with more potential

2022· article· en· W7002002852 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2022
Typearticle
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemMarine ecosystemPopulationClimate changeListing (finance)BiodiversityGlobal biodiversityBaseline (sea)
DOInot available

Abstract

fetched live from OpenAlex

Species at risk are native species, sub-species or ecologically significant units that warrant special attention to ensure their conservation. The number of marine species at risk within the Salish Sea is used by the US Environmental Protection Agency and Environment and Climate Change Canada as one of ten transboundary ecosystem indicators. Four jurisdictions within the Salish Sea have formal listing processes for marine species: the Province of British Columbia, the State of Washington, the Canadian Federal Government, and the United States Federal Government. As of October 15, 2021, there were 135 species listed as at risk in the Salish Sea: 4 invertebrates, 66 fish, 2 reptiles, 50 birds and 13 mammals. The list has been compiled periodically since 2002 when only 60 species were listed, and it has grown at each assessment. A portion of the growth in the number of listed species can be attributed to better information on the species that use the ecosystem or greater effort to assess species status, but for some species, additions reflect actual population declines in the Salish Sea over the last two decades. The list was pivotal in highlighting the magnitude of marine bird declines and motivating a taxa-wide risk assessment to identify underlying causes for declines in so many species, but overall has had little apparent value in driving ecosystem recovery. Rather than documenting the continued decline of species within the Salish Sea, this indicator could be better formalized to embrace the Drivers-Pressures-State-Impact-Response (DPSIR) approach as an organizing principle, as it has been for other transboundary ecosystem indicators. More formally engaging listing agencies and Tribal and First Nation co-managers to help detail the drivers and pressures behind listings could create a better indicator that facilitates design and implementation of transboundary conservation efforts that supersede a species-by-species piecemeal approach.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.209
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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