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Record W4393527015 · doi:10.5281/zenodo.5903190

Raw CyTOF images associated with Moldoveanu et al. 2022, Science Immunology

2022· dataset· en· W4393527015 on OpenAlexaff
Dan Moldoveanu, LeeAnn Ramsay, Mathieu Lajoie, Luke Anderson-Trocmé, Marine Lingrand, Diana Berry, Lucas J. M. Perus, Yuhong Wei, Cleber Moraes, Rached Alkallas, Shivshankari Rajkumar, Dongmei Zuo, Matthew Dankner, H. Eric Xu, Nicholas Bertos, Hamed S. Najafabadi, Simon Gravel, Santiago Costantino, Martin J. Richer, Amanda W. Lund, Sonia V. del Rincón, Alan Spatz, Wilson H. Miller, Rahima Jamal, Réjean Lapointe, Anne‐Marie Mes‐Masson, Simon Turcotte, Kevin Petrecca, Sinziana Dumitra, Ari N. Meguerditchian, Keith Richardson, Francine Tremblay, Béatrice Wang, May Chergui, Marie‐Christine Guiot, Kevin Watters, John Stagg, Daniela F. Quail, Catalin Mihalcioiu, Sarkis Meterissian, Ian R. Watson

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMontreal Neurological Institute and HospitalCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General HospitalMcGill University and Génome Québec Innovation CentreHôpital Maisonneuve-RosemontMcGill University Health Centre
Fundersnot available
KeywordsImmunologyBiologyComputational biology

Abstract

fetched live from OpenAlex

This dataset contains the raw CyTOF images associated with the paper "An In-depth Map of the Melanoma Immune Microenvironment and Correlates of Immunotherapy Response by Imaging Mass Cytometry" (Moldoveanu et al. 2022). The MCD files output by CyTOF IMC were exported to one TIFF per channel per sample. This dataset contains one directory per sample (see Supplementary Table S1 for additional information). Each directory contains 46 TIFF images, which includes the 35 channels used in our study, 1 TIFF of the cell segmentation mask, and 10 TIFFs associated with background or unused channels.

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)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.075
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0750.120

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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
Published2022
Admission routes1
Has abstractyes

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