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Record W2039754235 · doi:10.3390/su5093976

Global Aquaculture Performance Index (GAPI): The First Global Environmental Assessment of Marine Fish Farming

2013· article· en· W2039754235 on OpenAlexaff
John P. Volpe, Jennifer L. M. Gee, Valerie Ethier, Martina Beck, Amanda Wilson, Jenna Stoner

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsUniversity of Victoria
FundersJohns Hopkins UniversityPew Charitable Trusts
KeywordsAquacultureProduction (economics)Index (typography)BusinessFisheryNatural resource economicsAgricultureProduct (mathematics)Work (physics)Environmental resource managementFish farmingSustainabilityFish <Actinopterygii>EcologyEnvironmental scienceEconomicsBiologyEngineeringComputer science

Abstract

fetched live from OpenAlex

“Sustainable” is among the most sought after of all seafood product adjectives. Ironically it is also one of the most poorly defined and understood. The Global Aquaculture Performance Index (GAPI) is the first tool to assess environmental performance of global marine aquaculture production, permitting direct comparison of disparate species, production methods and jurisdictions. Clear patterns emerge from this analysis; significant variation of environmental performance is driven by the species being farmed, significant room for improvement exists across the entire sector, the worst performing players are also the fastest growing, particularly within Asia, and perhaps most importantly, this work highlights the potential trap awaiting policy makers who focus too narrowly on farm production efficiency alone as a solution to diminishing seafood availability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.236
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2013
Admission routes1
Has abstractyes

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