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Record W2585179645 · doi:10.1002/cyto.a.23053

Imaging Mass Cytometry

2017· editorial· en· W2585179645 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCytometry Part A · 2017
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenFluidigm (Canada)
Fundersnot available
KeywordsMass cytometryCytometryFlow cytometryBiomedical engineeringPathologyChemistryMaterials scienceMedicineImmunology

Abstract

fetched live from OpenAlex

Imaging Mass Cytometry (IMC) is an expansion of mass cytometry, but rather than analyzing single cells in suspension, it uses laser ablation to generate plumes of particles that are carried to the mass cytometer by a stream of inert gas. Images reconstructed from tissue sections scanned by IMC have a resolution comparable to light microscopy, with the high content of mass cytometry enabled through the use of isotopically labeled probes and ICP-MS detection. Importantly, IMC can be performed on paraffin-embedded tissue sections, so can be applied to the retrospective analysis of patient cohorts whose outcome is known, and eventually to personalized medicine. Since the original description in 2014, IMC has evolved rapidly into a commercial instrument of unprecedented power for the analysis of histological sections. In this Review, we discuss the underlying principles of this new technology, and outline emerging applications of IMC in the analysis of normal and pathological tissues. © 2017 International Society for Advancement of Cytometry.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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