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Record W2670786458 · doi:10.18174/409684

International stakeholder dialogue on pulse fisheries : report of the second dialogue meeting, Amsterdam, 20 January 2017

2017· report· en· W2670786458 on OpenAlexaff
Nathalie A. Steins, Sarah Lindley Smith, W.J. Strietman, Marloes Kraan, B.K. Trapman

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsImpact
Fundersnot available
KeywordsStakeholderFisheryPolitical sciencePulse (music)Library scienceBusinessGeographyPublic relationsComputer scienceTelecommunicationsBiology

Abstract

fetched live from OpenAlex

Fisheries Director at the Dutch Ministry of Economic Affairs welcomes a broad range of participants from eight countries to the dialogue meeting (for participant lists, see annex 1).She acknowledges that the Dutch are proud of the pulse trawl and consider it to be a sustainable alternative to the traditional beam-trawl.The Dutch have optimised the technique.But in the process, they forgot to take along the stakeholders and have not been so transparent.In their ambition, they have pushed on the development side of the trawl and getting the legislative approval.It appeared, however, that different groups of stakeholders from other countries had concerns; for example on impacts of fishing with electricity on marine organisms and control and enforcement.The Ministry of Economic Affairs therefore decided on a new approach that focusses on being transparent on benefits, questions and concerns.This process was started in July 2015 with the first international dialogue meeting held in Scheveningen.Many of the questions and concerns raised at that meeting then fed into the multi-annual research programme that is currently being carried out.In addition to the research side, a lot has happened on technique and on control and enforcement.At this dialogue meeting there will be parallel breakout sessions on:1. Into the working of pulse fishing -transition beam/pulse and scale models (section 2.2.1) 2. Setting the scene -research in hindsight and forecast (section 2.2.2) 3. Control & Enforcement -lessons learnt and developments (section 2.2.3) 4. Elaboration -Scope and regionalisation within EU -broader scope (section 2.2.4)

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)0.015
metaresearch head score (Gemma)0.010
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: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0090.006
Open science0.0020.011
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0250.007

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 head0.076
GPT teacher head0.265
Teacher spread0.189 · 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
GenreOther

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

Citations4
Published2017
Admission routes1
Has abstractyes

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