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Record W2734650562

A26961 - Internasjonalt forskernettverk innenfor fiskevelferd, atferd, stress og produktkvalitet i forbindelse med trenging av laks (NFR prosjektnr. 234048/E40)

2015· article· no· W2734650562 on OpenAlexaboutno aff
Hanne Digre, Ulf Erikson

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2015
Typearticle
Languageno
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsnot available
Fundersnot available
KeywordsStress (linguistics)PhysicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Resultatene prosjektet har oppnådd kan oppsummeres slik:
\n
\n• Det er etablert et internasjonalt nettverk innenfor fiskevelferd, atferd, stress og produktkvalitet av oppdrettslaks med forskere fra fem land; AVS Chile (Chile), Memorial University of Newfoundland og University of British Columbia (Canada), Plant & Food Research (New Zealand), IMARES Wageningen UR (Nederland), NTNU, NINA og SINTEF Fiskeri og havbruk (Norge).
\n
\n• Det er avholdt en internasjonal workshop i Vancouver i Canada fra 21 til 23. oktober 2014 med representanter fra forskning og akvakulturindustri, samt ekskursjon til oppdrettsanlegg og klekkeri.
\n
\n• Det er gjennomført et besøk til New Zealand, inklusive møter med to ulike forskningsinstitusjoner, samt møter med bedriften New Zealand King Salman hvor både klekkeri, oppdrettslokalitet og prosesseringsanlegg ble besøkt. 
\n
\n• Nettverket planlegger å søke Havbruksprogrammet (Forskningsrådet) i 2015 om et prosjekt med fokus på å identifisere effekter av trenging laks med hensyn på fiskevelferd, stress og kvalitet. I tillegg har det framkommet flere andre prosjektinitiativ som er aktuelle både nasjonalt og internasjonalt.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.004

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 head0.192
GPT teacher head0.375
Teacher spread0.182 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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