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Record W3143231276 · doi:10.1139/as-2019-0035

Review of the ecosystem approach in Cumberland Sound, Nunavut, Canada

2021· article· en· W3143231276 on OpenAlexaffvenueabout
Ross F. Tallman, Marianne Marcoux

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsFisheryEcosystemFisheries managementGeographyContext (archaeology)Marine ecosystemArcticEcologyEnvironmental scienceFishingBiology

Abstract

fetched live from OpenAlex

Historically, fisheries have been monitored at the individual stock level, without consideration to connectivity to other species or activities in the ecosystem. The ecosystem approach requires that the stock and fishery be seen in the context of predators, competitors, prey, by-catch impacts, other fisheries, and abiotic environmental variables so that management is holistic. In this study, we describe the components of the ecosystem approach applied in the scientific investigation of fisheries in Cumberland Sound, Nunavut. Relative to other Canadian Arctic locales with commercial fisheries operations, the Cumberland Sound area has a greater biodiversity and abundance of fishes and marine mammal species. These components support active fisheries for Arctic Charr, Greenland Halibut, and Beluga Whale, as well as Ringed, Bearded, and Harp Seals. The species and their fisheries are variable in character, their ecosystem effects, and their response to the environment. We describe the species dynamics and their fisheries within an ecosystem context. We briefly note the challenges to developing an overarching model of the system such as the integration of the different life histories of the species, as well as the incorporation of future non-fisheries-related disturbances.

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)0.001
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 score0.494
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.015
GPT teacher head0.225
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 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

Citations6
Published2021
Admission routes3
Has abstractyes

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