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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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Nature Methods
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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.

403 results · 1 filter active ·
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20042025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
403 works in the cohort · of 4,299,418page 3 of 9

Labels cover 1 of 403 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 403 of 403 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.

affno abstractunlabeled
eSGA: E. coli synthetic genetic array analysis
Gareth Butland, Mohan Babu, J. Javier Díaz-Mejía, Bohdana Fedyshyn, Sadhna Phanse, B. Gold +28 more
2008· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
257
citations
affno abstractunlabeled
Machine learning: a primer
Danilo Bzdok, Martin Krzywinski, Naomi Altman
2017· article· es· Nature Methods· Computer Science
machine prediction:candidate · noneconsensus · none
241
citations
affno abstractunlabeled
The SEIRS model for infectious disease dynamics
Ottar N. Bjørnstad, Katriona Shea, Martin Krzywinski, Naomi Altman
2020· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
237
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
affno abstractunlabeled
Replication
Paul C. Blainey, Martin Krzywinski, Naomi Altman
2014· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
224
citations
affno abstractunlabeled
Genome-wide SWAp-Tag yeast libraries for proteome exploration
Uri Weill, Ido Yofe, Ehud Sass, Bram Stynen, Dan Davidi, Janani Natarajan +19 more
2018· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
212
citations
affno abstractunlabeled
Optogenetic analysis of synaptic function
Jana Liewald, Martin Brauner, Greg J. Stephens, Magali Bouhours, Christian Schultheis, Mei Zhen +1 more
2008· article· en· Nature Methods· Neuroscience
machine prediction:candidate · noneconsensus · none
212
citations
affno abstractunlabeled
A practical guide to cancer subclonal reconstruction from DNA sequencing
Maxime Tarabichi, Adriana Salcedo, Amit G. Deshwar, Máire Ní Leathlobhair, Jeff Wintersinger, David C. Wedge +3 more
2021· review· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
201
citations
affno abstractunlabeled
Optogenetic control with a photocleavable protein, PhoCl
Wei Zhang, Alexander W. Lohman, Yevgeniya Zhuravlova, Xiaocen Lu, Matthew Wiens, Hiofan Hoi +7 more
2017· article· en· Nature Methods· Neuroscience
machine prediction:candidate · noneconsensus · none
190
citations
affno abstractunlabeled
Clustering
Naomi Altman, Martin Krzywinski
2017· article· en· Nature Methods· Computer Science
machine prediction:candidate · noneconsensus · none
186
citations
affno abstractunlabeled
Gene-pair expression signatures reveal lineage control
Matti Nykter, Roger Kramer, Anke Wienecke-Baldacchino, Lasse Sinkkonen, Joseph Zhou, Richard Kreisberg +3 more
2013· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
185
citations
affno abstractunlabeled
Significance, P values and t-tests
Martin Krzywinski, Naomi Altman
2013· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
185
citations
affno abstractunlabeled
Deep learning-based point-scanning super-resolution imaging
Linjing Fang, Fred Monroe, Sammy Weiser Novak, Lyndsey M. Kirk, Cara R. Schiavon, Seungyoon B. Yu +13 more
2021· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
179
citations
afffundno abstractunlabeled
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Ali Emre Kavur +63 more
2024· review· en· Nature Methods· Medicine
machine prediction:candidate · metaresearchconsensus · none
175
citations
affno abstractunlabeled
Visualizing genomes: techniques and challenges
Cydney Nielsen, Michael Cantor, Inna Dubchak, David Gordon, Ting Wang
2010· review· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
172
citations
affno abstractunlabeled
Simple linear regression
Naomi Altman, Martin Krzywinski
2015· article· en· Nature Methods· Decision Sciences
machine prediction:candidate · noneconsensus · none
172
citations

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