MétaCan
Menu
Back to cohort
Record W2032016414 · doi:10.1107/s0909049512039118

Accounting for low-frequency synchrotron X-ray beam position fluctuations for dynamic visualizations

2012· article· en· W2032016414 on OpenAlexafffundabout
James Hinebaugh, Priyanka Challa, Aimy Bazylak

Bibliographic record

VenueJournal of Synchrotron Radiation · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
FundersNational Research Council CanadaWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of TorontoCanadian Light Source
KeywordsSynchrotronPosition (finance)Synchrotron radiationBeam (structure)PhysicsOpticsMaterials scienceBusiness

Abstract

fetched live from OpenAlex

Synchrotron X-ray radiography on beamline 05B1-1 at the Canadian Light Source Inc. was employed to study dynamic liquid water transport in the porous electrode materials of polymer electrolyte membrane fuel cells. Dynamic liquid water distributions were quantified for each radiograph in a sequence, and non-physical liquid water measurements were obtained. It was determined that the position of the beam oscillated vertically with an amplitude of ~25 µm at the sample and a frequency of ~50 mHz. In addition, the mean beam position moved linearly in the vertical direction at a rate of 0.74 µm s(-1). No evidence of horizontal oscillations was detected. In this work a technique is presented to account for the temporal and spatial dependence of synchrotron beam intensity, which resulted in a significant reduction in false water thickness. This work provides valuable insight into the treatment of radiographic time-series for capturing dynamic processes from synchrotron radiation.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.243
Teacher spread0.239 · 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 designBench or experimental
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

Citations46
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Synchrotron RadiationSame topicFuel Cells and Related MaterialsFrench-language works237,207