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Record W2123125788 · doi:10.1139/f04-173

Reconciling overfishing and climate change with stock dynamics of Atlantic cod (<i>Gadus morhua</i>) over 500 years

2004· article· en· W2123125788 on OpenAlexfundvenueaboutno aff
George A. Rose

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGadusOverfishingAtlantic codFishingClimate changeFisheryGadidaeStock (firearms)PopulationEnvironmental scienceStock assessmentGeographyEcologyOceanographyBiologyDemographyGeology

Abstract

fetched live from OpenAlex

To examine overfishing and climate effects on depleted cod (Gadus morhua) stocks, a surplus production model based on reconstructions of cod catch in Newfoundland was used to describe biomass dynamics from 1505 to 2004. Productivity parameters r (population growth rate) and K (carrying capacity) were assigned by fitting model to survey biomass. Assumptions of fishery-only influences inferring constant, random, or depensatory parameters fared poorly (did not mimic history), as did climate influences indexed by tree ring growth. However, a model using both climate and depensation fared well, mimicking much documented history of Newfoundland cod, including declines during the Little Ice Age (mid- to late 19th century) and the stock collapses of the late 20th century, with a good fit to recent scientific surveys (r 2 = 0.80). This model suggests temporal differentiation between fishing and climate effects, including (i) declines during the Little Ice Age (1800–1880) caused by lower productivity, (ii) collapses in the 1960s caused by overfishing, (iii) collapses in the late 1980s caused by both, and (iv) rebuilding now hindered by depensatory effects of low numbers.

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 score0.141
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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

Citations197
Published2004
Admission routes3
Has abstractyes

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