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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Cell Image Analysis Techniques
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,696 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,696 works in the cohort · of 4,299,418page 1 of 74

Labels cover 7 of 3,696 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,696 of 3,696 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

afffundunlabeled
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin, T. Brian, Gregory P. Way +30 more
2018· review· en· Journal of The Royal Society Interface· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
2,238
citations
affno abstractunlabeled
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu +24 more
2023· review· en· Nature· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
1,594
citations
afffundno abstractunlabeled
High-throughput discovery of novel developmental phenotypes
Mary E. Dickinson, Ann M. Flenniken, Xiao Ji, Lydia Teboul, Michael D. Wong, Jacqueline K. White +78 more
2016· article· en· Nature· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1,294
citations
affunlabeled
BigBrain: An Ultrahigh-Resolution 3D Human Brain Model
Katrin Amunts, Claude Lepage, Louis Borgeat, Hartmut Mohlberg, Timo Dickscheid, Marc-Étienne Rousseau +8 more
2013· article· en· Science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
936
citations
affunlabeled
Data-analysis strategies for image-based cell profiling
Juan Carlos Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnár +18 more
2017· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
790
citations
afffundno abstractunlabeled
Neuronal morphometry directly from bitmap images
Tiago Ferreira, Arne V. Blackman, Julia Oyrer, Sriram Jayabal, Andrew J. Chung, Alanna J. Watt +2 more
2014· letter· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
783
citations
affno abstractunlabeled
Deep learning in biomedicine
Michael Wainberg, Daniele Merico, Andrew Delong, Brendan J. Frey
2018· article· en· Nature Biotechnology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
604
citations
affunlabeled
Digital Imaging in Pathology: Whole-Slide Imaging and Beyond
Farzad Ghaznavi, Andrew Evans, Anant Madabhushi, Michael D. Feldman
2011· review· en· Annual Review of Pathology Mechanisms of Disease· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
463
citations
affno abstractunlabeled
T-snakes: Topology adaptive snakes
Tim McInerney, Demetri Terzopoulos
2000· article· en· Medical Image Analysis· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
413
citations
affno abstractunlabeled
Tutorial: guidance for quantitative confocal microscopy
James Jonkman, Claire M. Brown, Graham Wright, Kurt I. Anderson, Alison J. North
2020· review· en· Nature Protocols· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
401
citations
affno abstractunlabeled
High-Content Screening for Quantitative Cell Biology
Mojca Mattiazzi Ušaj, Erin B. Styles, Adrian J. Verster, Helena Friesen, Charles Boone, Brenda Andrews
2016· review· en· Trends in Cell Biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
292
citations
affunlabeled
Robust estimation of bacterial cell count from optical density
Jacob Beal, Natalie G. Farny, Traci Haddock-Angelli, Vinoo Selvarajah, Geoff Baldwin, Russell Buckley-Taylor +1358 more
2020· article· en· Communications Biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
231
citations
afffundno abstractunlabeled
Reproducibility standards for machine learning in the life sciences
Benjamin J. Heil, Michael M. Hoffman, Florian Markowetz, Su‐In Lee, Casey S. Greene, Stephanie C. Hicks
2021· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · metaresearch
228
citations
afffundno abstractunlabeled
TensorFlow: Biology’s Gateway to Deep Learning?
Ladislav Rampášek, Anna Goldenberg
2016· article· en· Cell Systems· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
221
citations
affunlabeled
Global Linking of Cell Tracks Using the Viterbi Algorithm
Klas E. G. Magnusson, Joakim Jaldén, Penney M. Gilbert, Helen M. Blau
2014· article· en· IEEE Transactions on Medical Imaging· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
213
citations
affunlabeled
The human cell count and size distribution
Ian Hatton, Eric D. Galbraith, Nono S. C. Merleau, Teemu P. Miettinen, Benjamin M. Smith, Jeffery A. Shander
2023· article· en· Proceedings of the National Academy of Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
170
citations
affunlabeled
AI on a chip
Akihiro Isozaki, Jeffrey Harmon, Yuqi Zhou, Shuai Li, Yuta Nakagawa, Mika Hayashi +3 more
2020· review· en· Lab on a Chip· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
142
citations
affunlabeled
Intelligent image-activated cell sorting 2.0
Akihiro Isozaki, Hideharu Mikami, Hiroshi Tezuka, Hiroki Matsumura, Kangrui Huang, Marino Akamine +40 more
2020· article· en· Lab on a Chip· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
142
citations
affunlabeled
In silico cancer research towards 3R
Claire Jean-Quartier, Fleur Jeanquartier, Igor Jurišica, Andreas Holzinger
2018· review· en· BMC Cancer· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
128
citations

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