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Record W4393708377 · doi:10.5281/zenodo.1322047

Ibm (Meganyctiphanes Norvegica And Thysanoessa Raschii) -- Datasets

2018· dataset· en· W4393708377 on OpenAlexaffabout
Déborah Benkort, Maps Frédéric

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typedataset
Languageen
FieldComputer Science
TopicDigital Imaging for Blood Diseases
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIBMPhysicsOptics

Abstract

fetched live from OpenAlex

In this file are found the datasets used for the following study: Individual‐based modeling explains the contrasted seasonality in size, growth, and reproduction of the sympatric Arctic (Thysanoessa raschii) and Nordic krill (Meganyctiphanes norvegica) in the St. Lawrence Estuary, eastern Canada This study was published on 2018-09-14 (doi: 10.1002/lno.11032). The file MN_L&O_results.Rdata corresponds to the dataset for theMeganyctiphanes norvegica species, and the file TR_L&O_results.Rdata corresponds to the dataset for the Thysanoessa raschii species.

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)0.001
metaresearch head score (Gemma)0.003
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.037
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0370.061

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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
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 routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicDigital Imaging for Blood DiseasesFrench-language works237,207