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Record W2006949776 · doi:10.1139/f00-193

Fishing down Canadian aquatic food webs

2001· article· en· W2006949776 on OpenAlexvenueaboutno aff
Daniel Pauly, Maria Lourdes D. Palomares, Rainer Froese, Pascualita Sa-a, Michael Vakily, David Preikshot, Scott Wallace

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersEuropean CommissionPew Charitable Trusts
KeywordsFisheryFishingGadusPacific herringMerlucciusTrophic levelHakeGeographyGroundfishFisheries managementHerringClupeaEnvironmental scienceEcologyBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The mean trophic level (TL) of fish landed in fisheries on the east and west coasts of Canada is declining by 0.03–0.10·decade –1 , similar to global trends. This finding is based on data from United Nations Food and Agriculture Organization and the Canadian Department of Fisheries and Oceans and other Canadian sources for the period 1873–1997. Significant rates of decline in mean TL were obtained even when key species — Atlantic cod (Gadus morhua) on the east coast and Pacific herring (Clupea pallasi) and Pacific hake (Merluccius productus) on the west coast — were omitted from the analysis. Fish taken in inland water fisheries did not exhibit a decline in mean TL. Two models were developed, based on length and age, respectively, for correcting TL estimates of individual species for the effects of changes in body size due to changes in fishing mortality. Both produced corrections that were small relative to changes in mean TL that resulted from changes in species composition of the catch over time. Overall, these results suggest that the mean TL of fish landed can be used as an index of sustainability in multispecies fisheries and that its reliability will depend on the quality of the data and length of the time series available for analysis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.992

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.025
GPT teacher head0.226
Teacher spread0.200 · 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.

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

Citations252
Published2001
Admission routes2
Has abstractyes

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