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Record W6929237718 · doi:10.4224/17506221

Progress report: ship safety and performance in pressured ice zones

2009· report· en· W6929237718 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2009
Typereport
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsNational Research Council CanadaCanadian Wood Council
FundersTransport Canada
KeywordsWork (physics)ArcticThe arcticService (business)Sea iceInformation system

Abstract

fetched live from OpenAlex

The objective of this project is to provide real-time information to ships operating in the Arctic to minimize safety and operational problems due to pressured ice conditions. This will be done by providing real-time information and on-board predictive system to ship operators. The output will give ship captains information on the development of pressured ice along shipping routes and on locations of pressured ice. Such information is not available now. This project will develop and implement new technology to improve advising on ice pressure development along specific shipping lanes in the Arctic. Computer code will be developed by the Canadian Hydraulics Centre of National Research Council of Canada (NRC-CHC) and delivered to Canadian Ice Service (CIS), and potentially to ship operators. NRC-CHC will work with CIS and other partners on the implementation and testing of the system. This report describes progress made during the second year of the four-year project. Start date of the entire project was April 1, 2007 and the completion date is March 31, 2011.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.036
GPT teacher head0.340
Teacher spread0.304 · 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 designObservational
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
Published2009
Admission routes4
Has abstractyes

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