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Record W4312062511 · doi:10.5281/zenodo.6983141

D1.6. Modalities of European PRVs' ship- time collaborations and exchanges

2022· report· en· W4312062511 on OpenAlexfundno aff
Colin A. Stedmon, Miguel A. Ojeda, Justiina Dahl, Karen Edelvang, Stig Flått, Katarina Gårdfeldt, Mats A. Granskog, Verónica Willmott Puig

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typereport
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeNatural Environment Research CouncilUniversité Laval
KeywordsModalitiesComputer scienceEnvironmental scienceSociologySocial science

Abstract

fetched live from OpenAlex

Part of the ARICE project (WP1) focusses on investigating the feasibility of transnational coordination of Arctic research vessel use. Arctic research is expensive and there is much to be gained from reflecting on current practises and identifying future opportunities. Earlier ARICE deliverables have examined the current status of transnational access to Polar research vessels and how the vessel fleet capacity can be utilized optimally. The focus of this deliverable is to map how an EU initiative can facilitate transnational research expeditions. The current vessel fleet is summarised, and existing national and international initiatives are highlighted as inspiration towards a new effort for optimising polar research vessel use. A path forward is suggested which involves the formalisation of a sustained European funding framework for vessel charter which would be a great benefit to European research, international collaboration and facilitate the optimal use of research vessels across Europe and motivate individual nations to maintain their research vessel infrastructure.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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: Other · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2530.002

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 head0.042
GPT teacher head0.242
Teacher spread0.200 · 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
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
Published2022
Admission routes1
Has abstractyes

Explore more

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