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Record W2263103844 · doi:10.1093/icesjms/fsv225

Fisheries, the inverted food pyramid

2015· article· en· W2263103844 on OpenAlexaff
Jeppe Kolding, Alida Bundy, P.A.M. van Zwieten, Michael J. Plank

Bibliographic record

VenueICES Journal of Marine Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsTrophic levelEcosystemFishingFisheryMarine ecosystemTemperate climateEnvironmental scienceUpwellingEcologyBiology

Abstract

fetched live from OpenAlex

Abstract A global assessment of fishing patterns and fishing pressure from 110 different Ecopath models, representing marine ecosystems throughout the world and covering the period 1970–2007, show that human exploitation across trophic levels (TLs) is highly unbalanced and skewed towards low productive species at high TLs, which are around two TLs higher than the animal protein we get from terrestrial farming. Overall, exploitation levels from low trophic species were <15% of production, and only 18% of the total number of exploited groups and species were harvested >40% of their production. Generally, well-managed fisheries from temperate ecosystems were more selectively harvested at higher exploitation rates than tropical and upwelling (tropical and temperate) fisheries, resulting in potentially larger long-term changes to the ecosystem structure and functioning. The results indicate a very inefficient utilization of the food energy value of marine production. Rebuilding overfished components of the ecosystem and changing focus to balancing exploitation across a wider range of TLs, i.e. balanced harvesting, has the potential to significantly increase overall catches from global marine fisheries.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.788
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.039
GPT teacher head0.260
Teacher spread0.221 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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