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Record W2066259947 · doi:10.1371/journal.pone.0003881

Ecosystem Overfishing in the Ocean

2008· article· en· W2066259947 on OpenAlexafffund
Marta Coll, Simone Libralato, Sergi Tudela, Isabel Palomera, Fabio Pranovi

Bibliographic record

VenuePLoS ONE · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsOverfishingEcosystemFisheryFishingTrophic levelApex predatorEnvironmental scienceEcosystem healthMarine ecosystemEcologyEcosystem servicesBiology

Abstract

fetched live from OpenAlex

Fisheries catches represent a net export of mass and energy that can no longer be used by trophic levels higher than those fished. Thus, exploitation implies a depletion of secondary production of higher trophic levels (here the production of mass and energy by herbivores and carnivores in the ecosystem) due to the removal of prey. The depletion of secondary production due to the export of biomass and energy through catches was recently formulated as a proxy for evaluating the ecosystem impacts of fishing-i.e., the level of ecosystem overfishing. Here we evaluate the historical and current risk of ecosystem overfishing at a global scale by quantifying the depletion of secondary production using the best available fisheries and ecological data (i.e., catch and primary production). Our results highlight an increasing trend in the number of unsustainable fisheries (i.e., an increase in the risk of ecosystem overfishing) from the 1950s to the 2000s, and illustrate the worldwide geographic expansion of overfishing. These results enable to assess when and where fishing became unsustainable at the ecosystem level. At present, total catch per capita from Large Marine Ecosystems is at least twice the value estimated to ensure fishing at moderate sustainable levels.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.998

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)0.0030.000

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 head0.058
GPT teacher head0.213
Teacher spread0.154 · 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

Citations275
Published2008
Admission routes2
Has abstractyes

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