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Record W2314115803 · doi:10.1149/1.2729078

Stack Testing Summary - Versa Power Systems

2007· article· en· W2314115803 on OpenAlexaboutno aff
Jason Dueck, Sofiane Benhaddad, Casey Brown, Oliver Grande, James Kelsall, Todd Machacek, Jeff Nelson, Scott Thompson

Bibliographic record

VenueECS Transactions · 2007
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsnot available
FundersImperial College LondonU.S. Department of EnergyU.S. Department of Defense
KeywordsStack (abstract data type)EngineeringPower (physics)Computer scienceOperating system

Abstract

fetched live from OpenAlex

Versa Power Systems' stack development is based on a consistent, repeatable testing methodology focused on increasing the durability and endurance of the stack while simultaneously decreasing the stack manufacturing cost. Standard tests are repeated on every stack to allow for comparison of various stack configurations and material systems. These standard tests are also used as a qualification step to maximize the usefulness of long-term stack in-test stand and stack in system testing. Statistical analysis of the test results provides an opportunity for process improvement and optimization. Testing has been performed at Versa Power Systems (Calgary, Canada), Cummins Power Generation (Minneapolis, MN), Gas Technology Institute (Chicago, IL), U.S. National Energy Technology Laboratory (Morgantown, WV), U.S. Naval Undersea Warfare Center (Newport, RI), the U.S. Air Force Lab (Panama City, FL) and Imperial College (London, England) with a minimum variation in results. These results are presented and discussed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0850.020

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 head0.028
GPT teacher head0.278
Teacher spread0.249 · 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
Published2007
Admission routes1
Has abstractyes

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