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Record W4312062520 · doi:10.5281/zenodo.6907818

D4.5. Handbook on cruise prerequisites to access ARICE PRVs

2019· report· en· W4312062520 on OpenAlexaff
Miguel A. Ojeda, Verónica Willmott Puig, Doug Baird, Justiina Dahl, Stig Flått, Alexandre Forest, Mats A. Granskog, Anissa Merzouk, Asa Lidgren, Anja Sommerfeld, Hanna Suutarla

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typereport
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversité Laval
FundersHorizon 2020 Framework Programme
KeywordsCruiseComputer scienceAeronauticsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This deliverable addresses the necessary requirements to embark any of the ARICE research vessels as part of the Transnational Access Program. Taking these requirements into account is important to ensure that the embarking parties are properly informed in order to ease the logistic organization. Detailed information on each of the specific vessels is included in the Annex.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.222
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2220.174

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 head0.058
GPT teacher head0.292
Teacher spread0.234 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicMaritime Transport Emissions and EfficiencyFrench-language works237,207