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Record W4394220321 · doi:10.6084/m9.figshare.22605457

Additional file 1 of Spatiotemporal evolution of the clear cell renal cell carcinoma microenvironment links intra-tumoral heterogeneity to immune escape

2023· dataset· en· W4394220321 on OpenAlexaff
Mahdi Golkaram, Fengshen Kuo, Sounak Gupta, Maria I. Carlo, Michael Salmans, Raakhee Vijayaraghavan, Cerise Tang, Vlad Makarov, Phillip M. Rappold, Kyle A. Blum, Chen Zhao, Rami Mehio, Shile Zhang, Jim Godsey, Traci Pawlowski, Renzo G. DiNatale, Luc G.T. Morris, Jeremy C. Durack, Paul Russo, Ritesh R. Kotecha, Jonathan Coleman, Ying‐Bei Chen, Victor E. Reuter, Robert J. Motzer, Martin H. Voss, Li Liu, Ed Reznik, Timothy A. Chan, A. Ari Hakimi

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsTumor microenvironmentImmune systemTumour heterogeneityBiologyImmune escapeCellRenal cell carcinomaClear cell renal cell carcinomaCancer researchTumor heterogeneityImmunologyMedicinePathologyCancerGenetics

Abstract

fetched live from OpenAlex

Additional file1 Supplementary Tables: Cohort characteristics, gene signature scores, ITH classification and histopathology review results

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)0.013
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: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.439
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4390.100

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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 designObservational
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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