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Record W2059704432 · doi:10.1371/journal.pone.0016011

Fisheries and Marine Animal Populations: Learning from the Long Term

2011· article· en· W2059704432 on OpenAlexfundno aff
David J. Starkey, Tim D. Smith, Michaela Barnard

Post-publication record

NatureRetraction
ReasonEuphemisms for Plagiarism;Plagiarism of Text;
Date2/3/2011 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersDalhousie UniversityAlfred P. Sloan Foundation
KeywordsTerm (time)FisheryFisheries scienceBiologyEcologyOceanographyFisheries managementFishingGeology

Abstract

fetched live from OpenAlex

The articles that comprise the HMAP Collection are products of the History of Marine Animal Populations (HMAP) project.This is an international, multidisciplinary initiative, the overarching aim of which is to improve knowledge and understanding of the long-term interaction of humankind with the marine environment.HMAP endeavours to attain this goal in three principal ways.First, a concerted effort is being made to embed the approaches and methods developed by HMAP into the institutional fabric of the universities that are hosting the project.This connects closely with the second strand of the scheme, which is designed to develop the parallel disciplines of historical marine ecology and marine environmental history through the sponsorship of graduate studentships, workshops, summer schools, conferences and the dissemination of research outputs.The third activity is the co-ordination of a research programme embracing the efforts of over 100 scientists in 18 countries working in teams tasked with investigating 12 regionally-specific, two thematic and two global taxon-specific case studies [www. hmapcoml.org/projects].HMAP has progressed fruitfully in all three respects since its inception in 2000.It has established centres at the universities of New Hampshire (USA), Roskilde (Denmark), Hull (UK), Murdoch (Australia) and Trinity College Dublin (Ireland), where faculty members -some of whom might be cast as 'HMAP graduates' -are responsible for leading the project and cultivating its distinctive approach to marine environmental issues.Here, and at numerous other educational institutions, the curricula have been enriched by the introduction of programs of study that focus on the marine dimensions of historical ecology and environmental history.Such learning and teaching work is informed by research undertaken under the aegis of HMAP, which by 2009 had generated over 200 printed and online works [www. hmapcoml.org/publications],as well as a substantial web-based data store [1], an online atlas of fisheries in the case study areas [2] and an image gallery [3].The articles in the HMAP Collection add to that output.Some were generated by scientists funded as part of the HMAP research effort, while others had their genesis in papers presented at 'Oceans Past II: Multidisciplinary Perspectives on the History and Future of Marine Animal Populations', an international conference convened by HMAP and hosted by the Aquatic Ecosystem Research Laboratory at the University of British Columbia in May 2009.The Collection testifies to the vitality of the HMAP approach to the dynamic interaction of humankind and the marine environment.This overview explains how that approach evolved, identifies the research issues that lie at its heart, and outlines some of the contributions to knowledge and understanding that HMAP research has yielded.

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)0.009
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.015
Scholarly communication0.0110.023
Open science0.0020.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.108
GPT teacher head0.220
Teacher spread0.112 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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