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Record W2465663923 · doi:10.1002/fuce.201600060

Sensitivity Analysis of the Impedance Characteristics of Proton Exchange Membrane Fuel Cells

2016· article· en· W2465663923 on OpenAlexaff
Seyed Mohammad Rezaei Niya, Ryan K. Phillips, Mina Hoorfar

Bibliographic record

VenueFuel Cells · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsElectrical impedanceAnodeProton exchange membrane fuel cellCathodeSensitivity (control systems)Range (aeronautics)Materials scienceAnalytical Chemistry (journal)ChemistryMembraneElectrodeChromatographyComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract The sensitivity of the impedance characteristics to the operating conditions of the proton exchange membrane fuel cell is studied. The impedance of a cell is measured in different potentials (current densities), operating temperatures, and anode and cathode relative humidities to estimate the indifference interval of each of these parameters. The indifference interval of a parameter, defined for a specific point, refers to a range out of which the impedance results are statistically dissimilar from that specific point. The analysis presented evaluates the average indifference intervals of the potential, temperature and relative humidities to be 10 mV, 5 °C and 7%, respectively. In other words, the changes less than the above mentioned limits do not affect the impedance of the cell statistically.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations12
Published2016
Admission routes1
Has abstractyes

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