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Record W4394182843 · doi:10.6084/m9.figshare.24040663

Additional file 1 of Cross-species oncogenomics offers insight into human muscle-invasive bladder cancer

2023· dataset· en· W4394182843 on OpenAlexaff
Kim Wong, Federico Abascal, Latasha Ludwig, Heike Aupperle‐Lellbach, Julia Grassinger, Colin W. Wright, Simon J. Allison, Emma Pinder, Roger M. Phillips, Laura P. Romero, Arnon Gal, Patrick J. Roady, Isabel Pires, Franco Guscetti, John S. Munday, Maria C. Peleteiro, Carlos Pinto, Tânia Carvalho, João Cota, Elizabeth C. Du Plessis, Fernando Constantino‐Casas, Stephanie Plog, Lars Moe, Simone de Brot, Ingrid Bemelmans, Renée Laufer Amorim, Smitha R. Georgy, Justina Prada, Jorge del Pozo, Marianne Heimann, Louisiane de Carvalho Nunes, Outi Simola, P. Pazzi, Johan Steyl, Rodrigo Ubukata, Péter Vajdovich, Simon L. Priestnall, Alejandro Suárez‐Bonnet, Franco Roperto, Francesca Millanta, Chiara Palmieri, Ana Liza Ortiz, Cláudio S.L. Barros, Aldo Gava, Minna E. Söderström, Marie O’Donnell, Robert Klopfleisch, Andrea Manrique-Rincón, Iñigo Martincorena, Ingrid Ferreira, Mark J. Arends, Geoffrey A. Wood, David J. Adams, Louise van der Weyden

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBladder cancerCancerComputer scienceBiologyMedicineComputational biologyInternal medicine

Abstract

fetched live from OpenAlex

Additional file 1: Table S1. Clinical details of the patients in the study.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.481
Threshold uncertainty score0.741

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4810.090

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 head0.065
GPT teacher head0.334
Teacher spread0.269 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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