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2010· article· en· W4210458477 on OpenAlexaboutno aff

Bibliographic record

VenueConservation Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryWildlifeOncorhynchusShoreCover (algebra)TourismWildlife conservationGeographyFish <Actinopterygii>Environmental resource managementEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Cover description: Sockeye Salmon returning to their spawning stream in British Columbia, Canada. Conservation Scientists have spawned a proposal about how sockeye and other salmon species can be managed differently: harvesting fewer might bring more benefits not only to the ecosystem but also to the economy. While over a hundred vertebrate and countless other species depend on salmon, fisheries often take more salmon than all of these animals combined, even from runs bound for protected areas that were created to safeguard these wildlife. The proposal calls for shifts to harvesting in which fisheries take smaller catches of known runs closer to shore, strategies known to generate higher prices and more employment. The proposal also suggests that less commercially valuable pink and chum salmon might be worth more alive than dead in areas with salmon‐dependent eco‐tourism. Anticipating resistance from harvesting interests, spawning runs bound for terrestrial parks are identified as the best candidates for early implementation, and on a graduated schedule.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.470
Threshold uncertainty score0.934

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7690.460

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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