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Record W3160528426 · doi:10.2172/1782589

Next Generation Hydrogen Station Composite Data Products: Retail Stations; Summer 2020 (Q2 FY2020)

2021· report· en· W3160528426 on OpenAlexaboutno aff
Genevieve Saur, Spencer Gilleon, Sam Sprik

Bibliographic record

Venuenot available
Typereport
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsnot available
FundersNational Renewable Energy Laboratory
KeywordsSoftware deploymentReliability (semiconductor)Composite numberQuarter (Canadian coin)Hydrogen productionEnvironmental scienceData qualityComponent (thermodynamics)HydrogenEnvironmental economicsComputer scienceBusinessService (business)MarketingChemistryGeography

Abstract

fetched live from OpenAlex

This publication includes 102 composite data products (CDPs) produced for next generation hydrogen stations, with data through the second quarter of 2020. These CDPs include data from retail stations only and represent the following analysis categories: deployment, safety, maintenance and reliability, performance, cost, utilization, hydrogen quality, and component energy.

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)0.004
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.090
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0900.075

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 head0.671
GPT teacher head0.482
Teacher spread0.189 · 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
GenreDataset

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

Citations1
Published2021
Admission routes1
Has abstractyes

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