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Record W4404566651 · doi:10.1038/s41467-024-54306-x

The commitment of the human cell atlas to humanity

2024· review· en· W4404566651 on OpenAlexaff
Ido Amit, Kristin Ardlie, Fabiana Arzuaga, Gordon A. Awandare, Gary D. Bader, Alexander Bernier, Piero Carninci, Stacey Donnelly, Roland Eils, Alistair R. R. Forrest, Henry T. Greely, Roderic Guigó, Nir Hacohen, Muzlifah Haniffa, Emily Kirby, Bartha Maria Knoppers, Arnold R. Kriegstein, Ed S. Lein, Sten Linnarsson, Partha P. Majumder, Miriam Mérad, Kerstin B. Meyer, Musa M. Mhlanga, Garry P. Nolan, Ntobeko Ntusi, Dana Pe’er, Shyam Prabhakar, Maili Raven-Adams, Aviv Regev, Orit Rozenblatt‐Rosen, Senjuti Saha, Andrea Saltzman, Alex K. Shalek, Jay W. Shin, Hendrik G. Stunnenberg, Sarah A. Teichmann, Timothy L. Tickle, Alexandra–Chloé Villani, Christine A. Wells, B Wold, Huanming Yang, Xiaowei Zhuang

Bibliographic record

VenueNature Communications · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsMcGill UniversityUniversity of Toronto
FundersNational Cancer Institute
KeywordsGlobeHumanityAtlas (anatomy)Equity (law)General partnershipHuman diseaseAction (physics)Human healthEnvironmental ethicsPolitical scienceBiologyMedicineDiseasePathologyNeuroscienceLawEnvironmental health

Abstract

fetched live from OpenAlex

The Human Cell Atlas (HCA) is a global partnership “to create comprehensive reference maps of all human cells—the fundamental units of life – as a basis for both understanding human health and diagnosing, monitoring, and treating disease.” ( https://www.humancellatlas.org/ ) The atlas shall characterize cells from diverse individuals across the globe to better understand human biology. HCA proactively considers the priorities of, and benefits accrued to, contributing communities. Here, we lay out principles and action items that have been adopted to affirm HCA’s commitment to equity so that the atlas is beneficial to all of humanity. The Human Cell Atlas (HCA) aims to characterize cells from diverse individuals across the globe to better understand human biology. Here, the authors lay out principles and action items that have been adopted to affirm HCA’s commitment to equity so that the atlas is beneficial to all of humanity.

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)0.009
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0030.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0070.008

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 head0.031
GPT teacher head0.386
Teacher spread0.356 · 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
GenreReview

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

Citations19
Published2024
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicCell Image Analysis TechniquesFrench-language works237,207