MétaCan
Menu
Back to cohort
Record W2397341616 · doi:10.1016/j.marpol.2016.04.046

Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility

2016· article· en· W2397341616 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMarine Policy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersAllen FoundationPaul G. Allen Family FoundationMAVA FoundationPew Charitable Trusts
KeywordsDiscardsExclusive economic zoneSubsistence agricultureGeographyRecreationFisheryBusinessMarine conservationEnvironmental resource managementFishingPolitical scienceEconomicsAgriculture

Abstract

fetched live from OpenAlex

In 2005, the Sea Around Us described a website ( www.seaaroundus.org ) which presented, for all maritime countries and large marine ecosystems in the world, one of the most basic information items required by policy makers and fisheries managers: what catch was taken within their jurisdictional boundaries, and which countries took it. Surprisingly, for many countries this kind of jurisdictionally and/or ecologically assigned data had not been readily available before then. Since the release of these spatialized data, this material has had major influence on how fisheries are perceived by policy makers in various countries and by the global scientific community, as well as by a growing list of other stakeholders such as non-governmental environmental organizations and the general public. Here, the Sea Around Us updates the fisheries science , policy, conservation and management audience on the extensively modified spatial allocation method and a substantially improved new website. Also, this contribution points to and describes the much improved catch data underlying this website. These data now account for catches for all countries in the world by fisheries sectors (industrial, artisanal, subsistence, recreational), after augmenting the officially reported landings data through the inclusion of comprehensively reconstructed data of previously unreported catches and major discards, for every maritime country or territory in the world, and their Exclusive Economic Zone (EEZ). Also presented are the extensively improved spatial allocation procedures which assign global catch data to the 180,000 half degree spatial cells used by the Sea Around Us to subdivide the global ocean. The reconstructed data for 1950–2010 for all countries in the world and the High Seas, freely accessible and downloadable through the Sea Around Us web portal, will be updated regularly. It is hoped that these revised data and the substantially improved web utility will invigorate and assist the debate about the role of fisheries in a global framework as well as in national food security settings.

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.

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 score0.370
Threshold uncertainty score0.995

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 communication0.0000.001
Open science0.0010.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.299
Teacher spread0.264 · 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