MétaCan
Menu
Back to cohort
Record W2020170202 · doi:10.1371/journal.pone.0015143

The Spatial Expansion and Ecological Footprint of Fisheries (1950 to Present)

2010· article· en· W2020170202 on OpenAlexaff
Wilf Swartz, Enric Sala, S Tracey, Reg Watson, Daniel Pauly

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersWaitt FoundationNational Geographic SocietyPew Charitable Trusts
KeywordsFishingFisheryLatitudeFisheries managementGeographyClimate changeOceanographyLongitudeOceanic climateEnvironmental scienceBiologyGeology

Abstract

fetched live from OpenAlex

Using estimates of the primary production required (PPR) to support fisheries catches (a measure of the footprint of fishing), we analyzed the geographical expansion of the global marine fisheries from 1950 to 2005. We used multiple threshold levels of PPR as percentage of local primary production to define 'fisheries exploitation' and applied them to the global dataset of spatially-explicit marine fisheries catches. This approach enabled us to assign exploitation status across a 0.5° latitude/longitude ocean grid system and trace the change in their status over the 56-year time period. This result highlights the global scale expansion in marine fisheries, from the coastal waters off North Atlantic and West Pacific to the waters in the Southern Hemisphere and into the high seas. The southward expansion of fisheries occurred at a rate of almost one degree latitude per year, with the greatest period of expansion occurring in the 1980s and early 1990s. By the mid 1990s, a third of the world's ocean, and two-thirds of continental shelves, were exploited at a level where PPR of fisheries exceed 10% of PP, leaving only unproductive waters of high seas, and relatively inaccessible waters in the Arctic and Antarctic as the last remaining 'frontiers.' The growth in marine fisheries catches for more than half a century was only made possible through exploitation of new fishing grounds. Their rapidly diminishing number indicates a global limit to growth and highlights the urgent need for a transition to sustainable fishing through reduction of PPR.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.031
GPT teacher head0.228
Teacher spread0.197 · 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

Citations378
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicMarine and fisheries researchFrench-language works237,207