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Record W3045482397 · doi:10.1039/d0sc02241a

<i>In situ</i> tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

2020· article· en· W3045482397 on OpenAlexafffund
Michael Woolman, Jimmy Qiu, Claudia M. Kuzan-Fischer, Isabelle Ferry, Delaram Dara, Lauren Katz, Fowad Daud, Megan Wu, Manuela Ventura, Nicholas Bernards, Harley Chan, Inga B. Fricke, Mark Zaidi, Bradly G. Wouters, James T. Rutka, Sunit Das, Jonathan Irish, Robert Weersink, Howard J. Ginsberg, David A. Jaffray, Arash Zarrine‐Afsar

Bibliographic record

VenueChemical Science · 2020
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenPrincess Margaret Cancer CentreSickKids FoundationUniversity Health NetworkUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of CanadaHospital for Sick Children
KeywordsMass spectrometryIn situMass spectrometry imagingTracking (education)PathologyAnalytical Chemistry (journal)Computer scienceChemistryMedicineChromatographyPsychology

Abstract

fetched live from OpenAlex

tissue during tumor bed examination to assess cancer removal. The interface developed herein for the analysis and the display of spatially encoded PIRL-MS data can be adapted to other hand-held mass spectrometry analysis probes currently available.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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

Citations29
Published2020
Admission routes2
Has abstractyes

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