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Record W6891479270 · doi:10.4224/40002962

NRCan hydrogen codes and standards gap analysis project (NRCan H2 CSGA)

2022· report· en· W6891479270 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaGDG Environnement
Fundersnot available
KeywordsSoftware deploymentPillarHydrogenTheme (computing)

Abstract

fetched live from OpenAlex

This document was generated as an output for an NRC project that supports the deployment of Canada’s Hydrogen Strategy for Canada, which provides sector-wide theme recommendations applicable to eight pillars. This report specifically falls under the fourth pillar related to codes and standards, and it proposes updating existing codes and standards and creating new codes and standards to keep up with industry innovation and remove barriers to the deployment of hydrogen in Canada’s economic sectors.

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)0.011
metaresearch head score (Gemma)0.030
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: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.419

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.009
Science and technology studies0.0050.001
Scholarly communication0.0060.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0330.012

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 head0.055
GPT teacher head0.356
Teacher spread0.302 · 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
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
Published2022
Admission routes3
Has abstractyes

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