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Record W7071107539

Recovering yelloweye rockfish and bocaccio in the Salish Sea: a collaborative, long-term, multi-pronged approach

2022· article· en· W7071107539 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRockfishEndangered speciesThreatened speciesWildlifePopulationMarine conservationHabitatStakeholderKelp forestHabitat destruction
DOInot available

Abstract

fetched live from OpenAlex

Recovering yelloweye rockfish and bocaccio in the Salish Sea: a collaborative, long-term, multi-pronged approach Yelloweye rockfish and bocaccio occupying the Salish Sea have been listed under the U.S. Federal Endangered Species Act (ESA) since 2010, yelloweye as Threatened and bocaccio as Endangered. In 2017, NOAA Fisheries completed a recovery plan for both species, outlining critical data needs and collaborative policy actions to further recovery. In 2020, the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) assessed the inside waters (i.e., Salish Sea) population of yelloweye as Threatened, a change from the Special Concern status conferred in 2008. Here, I briefly describe several dedicated, collaborative field and laboratory studies on both sides of the international border since 2017; outline remaining data gaps and research opportunities; highlight creation of the Puget Sound Kelp Conservation and Recovery Plan as a tool for assessing rockfish habitat; and describe targeted outreach and engagement activities, including creation of a children’s book on rockfish biology and conservation. While environmental degradation from a suite of broad-scale forcing factors continues to threaten these species, systematic implementation of strategic recovery plans has bolstered understanding of species biology and ecological role, informing proactive management and stakeholder engagement. Continued, cooperative effort to fill information gaps and methodically address threats at both the species and ecosystem level is required to ensure recovery goals are met.

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)0.032
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0070.003
Scholarly communication0.0050.007
Open science0.0050.017
Research integrity0.0030.004
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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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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