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Record W4393429433 · doi:10.5281/zenodo.2025778

Krill production circuit's - DATASET

2018· dataset· en· W4393429433 on OpenAlexaboutno aff
Déborah Benkort

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typedataset
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsnot available
Fundersnot available
KeywordsKrillProduction (economics)FisheryOceanographyBiologyGeologyEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Here is found the outputs datafile from the simulation runs for the following study: Arctic krill (<em>Thysanoessa rachii</em>) and Nordic krill (<em>Meganyctiphanes norvegica</em>) circuits of production revealed by t he interactions between their physiology, swimming behaviour and the circulation in the sub-arctic Gulf of St-Lawrence, Canada. This study is presently in submitted state in the journal Progress in Oceanography The datafiles titled STATIC * correspond to the data used for the static simulations for both species (see description into the paper submitted) The datafiles titled TR_* correspond to the data used for the transport simulation for <em>Thysanoessa rachii</em> species (see description into the paper submitted) The datafiles titled MN_* correspond to the data used for the transport simulation for <em>Meganyctiphanes norvegica</em> species (see description into the paper submitted)

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.025

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 head0.049
GPT teacher head0.247
Teacher spread0.198 · 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
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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