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Record W7115603276 · doi:10.4224/40003933

NRC-OCRE flat plate apparatus development and commissioning

2025· report· en· W7115603276 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2025
Typereport
Languageen
Field
Topic
Canadian institutionsInstitut National de la Recherche ScientifiqueNational Research Council Canada
Fundersnot available
KeywordsHullTowingDragShipbuildingWork (physics)Flow (mathematics)

Abstract

fetched live from OpenAlex

This technical report describes the work conducted by the National Research Council of Canada (NRC) to develop and implement a flat plate apparatus in order to carry out canonical flow tests for evaluating the effect of ship hull finishes on skin frictional drag. First, the background of the project and an overview of flat plate testing in hydrodynamic laboratories are provided. Next, the NRC-OCRE flat plate apparatus is described, followed by the commissioning experiments and measurements that were carried out using two nominally identical flat plates painted with “model paint”. The results of the measurements are presented and discussed. Non-frictional drag components are estimated, using analytical, empirical and numerical methods. The measured plate resistance is compared with theoretical formulas. Uncertainty analysis is conducted and the impact of uncertainty on comparative experiments of finishes considered. Finally, the theory and process for extrapolating the results of plate towing tests to full-scale ship hull frictional resistance are presented.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.008

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 head0.045
GPT teacher head0.311
Teacher spread0.266 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2025
Admission routes3
Has abstractyes

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