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Small‐scale Arctic charr <i>Salvelinus alpinus</i> fisheries in Canada's Nunavut: management challenges and options

2011· article· en· W1967301339 on OpenAlexaffabout
Marie‐Julie Roux, R. F. Tallman, Christopher W. Lewis

Bibliographic record

VenueJournal of Fish Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSalvelinusFish migrationArctic charFisheryFisheries managementArcticStock assessmentBiologyOverfishingEnvironmental resource managementEcologyFishingTroutFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The Arctic charr Salvelinus alpinus is a diverse and abundant resource in Canada's Nunavut. The anadromous form is primarily targeted by exploitation in small-scale fisheries. The continued importance of subsistence fisheries and growing interest in further developing commercial fisheries underline the need for proper management of S. alpinus in northern Canada. This paper presents the current state of S. alpinus fisheries in Nunavut and related management challenges. An alternate framework for assessment using life-history information as it determines stock productivity and resilience to harvesting is presented. This framework combines (1) a risk assessment tool [productivity-susceptibility analysis (PSA)] to evaluate the relative vulnerability of S. alpinus stocks to harvest and (2) a conceptual model for quantitative assessment to determine sustainable harvest levels. Diversity in S. alpinus life history and contrast in vulnerability scores derived from PSA assessment are demonstrated for a sample of 86 anadromous stocks from throughout Nunavut. These data provide evidence in support of an alternate strategy for assessment permitting to integrate diversity in S. alpinus life history for improved generalization and representativeness. Salvelinus alpinus fisheries in Arctic regions exemplify the need for stock assessment and management alternatives to ensure fish conservation in remote, sensitive ecosystems and in data-poor circumstances.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.913
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.193
Teacher spread0.167 · 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 teacher head, 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

Citations54
Published2011
Admission routes2
Has abstractyes

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