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Record W4246761184 · doi:10.1016/s1464-2859(20)30475-2

Ballard unveils FCgen-HPS automotive stack and MOU with Audi, Mahle collaboration

2020· article· en· W4246761184 on OpenAlexaboutno aff

Bibliographic record

VenueFuel Cells Bulletin · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsPowertrainEngineeringAutomotive industryTruckMemorandum of understandingAutomotive engineeringLaunchedHeavy dutyElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Canadian-based Ballard Power Systems has launched its FCgen®-HPS PEM fuel cell stack, with an industry-leading volumetric high power density of 4.3 kW/L, to provide propulsion for a range of light-, medium- and heavy-duty vehicles. This was announced alongside a Memorandum of Understanding with German automaker Audi to expand its right to use the FCgen-HPS in all applications, including commercial trucks and passenger cars. Ballard is also collaborating with German automotive components supplier Mahle to develop and commercialise fuel cell systems for truck powertrains.

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)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.089
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0890.036

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 head0.004
GPT teacher head0.154
Teacher spread0.150 · 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
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

Citations1
Published2020
Admission routes1
Has abstractyes

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