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Record W2594603641 · doi:10.1080/03632415.2016.1276331

Salient Needs for Conservation of Atlantic Salmon

2017· article· en· W2594603641 on OpenAlexaffabout
R. J. Gibson

Bibliographic record

VenueFisheries · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersWorld Wildlife Fund
KeywordsSalientFisheryGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Abstract A short review is presented on the major factors contributing to recent precipitous declines in populations of wild Atlantic Salmon Salmo salar, with the approach of describing the major needs for stabilizing or enhancing factors to conserve and reverse the decline of salmon populations and incidentally of other salmonid species. Some aspects of physiology and required habitat characteristics through the life history of Atlantic Salmon are reviewed that determine responses to degradation of habitats. Anthropogenic developments, including obstructions to migration and degradation of freshwater habitats, are major reasons for declines in the resource. Thus, habitat is a primary factor to be considered in conservation and restoration. Socioeconomic considerations may override ecological and public interest concerns, and examples are given from Canada, where environmental regulations have been relaxed in favor of economic interests. Public education and awareness and advocacy in order for political “will” for better conservation of the resource are required to slow, and eventually stop, the decline in Atlantic Salmon populations.

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)0.003
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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