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Record W3185576702

Kosten en baten van schepen die in VK-wateren vissen

2019· article· de· W3185576702 on OpenAlexaff
J.A.E. van Oostenbrugge, J.B.M. op de Weegh, J.L. Roskam

Bibliographic record

VenueSocio-Environmental Systems Modeling · 2019
Typearticle
Languagede
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsImpact
Fundersnot available
KeywordsDie (integrated circuit)Computer science
DOInot available

Abstract

fetched live from OpenAlex

Deze factsheet geeft een inschatting van de kosten en opbrengsten per week voor vier groepen schepen die juridisch en qua visserijmogelijkheden in de wateren van het Verenigd Koninkrijk (VK) kunnen vissen: • schepen met een motorvermogen <=300 pk (met uitzondering van garnalenschepen) • schepen die gebruikmaken van de flyshootvismethode met een motorvermogen van >300 pk • schepen met een motorvermogen >300 pk met andere vismethoden (voornamelijk pulstrawl en boomkor) • pelagische trawlers.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0510.012

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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