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Record W4362530952 · doi:10.1158/1541-7786.22527308

Table S1 from Mitochondrial Aconitase ACO2 Links Iron Homeostasis with Tumorigenicity in Non–Small Cell Lung Cancer

2023· preprint· en· W4362530952 on OpenAlexafffund
Shideh Mirhadi, Wen Zhang, Nhu‐An Pham, Fereshteh Karimzadeh, Melania Pintilie, Jiefei Tong, Paul Taylor, Jonathan R. Krieger, Bethany Pitcher, Jenna Sykes, Leanne Wybenga-Groot, Christopher Fladd, Jing Xu, Tao Wang, Michael Cabanero, Ming Li, Jessica Weiss, Shingo Sakashita, Olga Zaslaver, Man Yu, Amy A. Caudy, Julie St‐Pierre, Cynthia Hawkins, Thomas Kislinger, Geoffrey Liu, Frances A. Shepherd, Ming‐Sound Tsao, Michael F. Moran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcGill University Health CentreUniversity of TorontoUniversity Health NetworkUniversity of OttawaPrincess Margaret Cancer CentreHospital for Sick Children
FundersNatural Sciences and Engineering Research Council of CanadaScheme for Promotion of Academic and Research CollaborationCanada Research ChairsGenome CanadaCanadian Institutes of Health ResearchGenome British Columbia
KeywordsAconitaseIron homeostasisHomeostasisLung cancerCancer researchCellCancerLungTable (database)MitochondrionInternal medicineBiologyMedicinePathologyChemistryMetabolismCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Statistical association of clinical and pathologic characteristics with engraftment

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)0.017
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 score0.628
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6280.108

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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 routes2
Has abstractyes

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Same topicRadiomics and Machine Learning in Medical ImagingFrench-language works237,207