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Record W4412814200 · doi:10.3897/bdj.13.e157371

A worldwide geographical scheme for recording the distribution of marine biota: proposal and call for feedback

2025· article· en· W4412814200 on OpenAlexaff
Nicolas Bailly, Serge Gofas, Britt Lonneville

Bibliographic record

VenueBiodiversity Data Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersMuséum National d'Histoire NaturelleYale University
KeywordsBiotaDistribution (mathematics)Scheme (mathematics)Environmental resource managementGeographyEcologyComputer scienceEnvironmental scienceBiologyMathematics

Abstract

fetched live from OpenAlex

This paper describes a project aimed at creating a worldwide set of polygons for recording marine distribution data, parallel to the current World Geographic Scheme for Recording Plant Distribution used on land. The countries' Exclusive Economic Zones were either taken as recording units or subdivided according to Marine Ecosystems of the World or the IHO Limits of Oceans and Seas when appropriate; existing local schemes were adopted for Europe and Australia. A hierarchical set of five Level-1 units, 26 Level-2 units, 232 Level-3 units and 536 Level-4 units is presented for feedback and intended to be submitted as a standard to the Biodiversity Information Standards (TDWG). This project is expected to provide a means to instantly retrieve national checklists for any taxonomic group and also a valuable tool to handle imprecise country-level records from the old literature.

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)0.103
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.103
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.121
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.014
Science and technology studies0.0030.002
Scholarly communication0.0060.010
Open science0.0070.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0320.021

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 head0.030
GPT teacher head0.257
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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