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Record W4394158239 · doi:10.6084/m9.figshare.22601792

Additional file 12 of Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD

2023· dataset· en· W4394158239 on OpenAlexaff
Qiqing Huang, Yuanyuan Wang, Lili Zhang, Wei Qian, Shaoran Shen, Jingshen Wang, Shuangshuang Wu, Wei Xu, Bo Chen, Mingyan Lin, Jianqing Wu

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsTranscriptomeCOPDComputational biologyMonocyteCellMicrobiologyBiologyComputer scienceImmunologyMedicineGeneticsGeneGene expressionInternal medicine

Abstract

fetched live from OpenAlex

Additional file 12: Dataset 10. Lists for genes expressed in endothelial cells which are strongly correlated with genes from macrophages (Spearman correlation coefficient ≥ 0.8 or ≤ -0.8), related to Fig. 6H. Lists for genes expressed in macrophages which are strongly correlated with the 8 dysregulated sphingolipid metabolic enzyme genes in macrophages (Spearman correlation coefficient ≥ 0.8 or ≤ -0.8), related to Fig. 6H.

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)0.010
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 score0.360
Threshold uncertainty score0.912

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
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)0.3600.089

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 head0.048
GPT teacher head0.225
Teacher spread0.177 · 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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