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Effects of Climate Change and UV Radiation on Fisheries for Arctic Freshwater and Anadromous Species

2006· article· en· W2174364235 on OpenAlexaff
James D. Reist, Frederick J. Wrona, Terry D. Prowse, J. Brian Dempson, Michael Power, Günter Köck, Theresa J. Carmichael, Chantelle D. Sawatzky, Hannu Lehtonen, Ross F. Tallman

Bibliographic record

VenueAMBIO · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsFish migrationFisheryClimate changeFisheries managementSubsistence agricultureArcticSustainabilityFishingEcologyEnvironmental resource managementEnvironmental scienceFish <Actinopterygii>BiologyAgriculture

Abstract

fetched live from OpenAlex

Fisheries for arctic freshwater and diadromous fish species contribute significantly to northern economies. Climate change, and to a lesser extent increased ultraviolet radiation, effects in freshwaters will have profound effects on fisheries from three perspectives: quantity of fish available, quality of fish available, and success of the fishers. Accordingly, substantive adaptation will very likely be required to conduct fisheries sustainably in the future as these effects take hold. A shift to flexible and rapidly responsive 'adaptive management' of commercial fisheries will be necessary; local land- and resource-use patterns for subsistence fisheries will change; and, the nature, management and place for many recreational fisheries will change. Overall, given the complexity and uncertainty associated with climate change and related effects on arctic freshwaters and their biota, a much more conservative approach to all aspects of fishery management will be required to ensure ecosystems and key fished species retain sufficient resiliency and capacity to meet future changes.

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)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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

Citations50
Published2006
Admission routes1
Has abstractyes

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Same venueAMBIOSame topicFish Ecology and Management StudiesFrench-language works237,207