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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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Trends in Pharmacological Sciences
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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.

140 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.
140 works in the cohort · of 4,299,418page 2 of 3

Labels cover 1 of 140 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 140 of 140 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
Cerebral vasospasm: looking beyond vasoconstriction
Jacob Hansen‐Schwartz, Peter Vajkoczy, R MACDONALD, R PLUTA, John H. Zhang
2007· article· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
109
citations
affno abstractunlabeled
Estrogen and cognitive aging in women
Barbara B. Sherwin
2002· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
108
citations
affno abstractunlabeled
Inhibitors of tissue transglutaminase
Jeffrey W. Keillor, Kim Yang-Ping Apperley, Abdullah Akbar
2014· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
104
citations
afffundunlabeled
Direct in vivo CAR T cell engineering
Lauralie Short, Robert A. Holt, Pieter R. Cullis, Laura Evgin
2024· article· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
Yeast chemical genomics and drug discovery: an update
Shawn Hoon, Robert P. St.Onge, Guri Giaever, Corey Nislow
2008· review· en· Trends in Pharmacological Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
96
citations
affno abstractunlabeled
Towards a pharmacology of DNA methylation
Moshe Szyf
2001· review· en· Trends in Pharmacological Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
94
citations
affno abstractunlabeled
Regulation of Nav channels in sensory neurons
Mohamed Chahine, Rahima Ziane, Kausalia Vijayaragavan, Yasushi Okamura
2005· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
77
citations
afffundno abstractunlabeled
Present and future of microglial pharmacology
Eva Šimončičová, Elisa Gonçalves de Andrade, Haley A. Vecchiarelli, Ifeoluwa O. Awogbindin, Charlotte Delage, Marie‐Ève Tremblay
2022· review· en· Trends in Pharmacological Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
75
citations
affno abstractunlabeled
Cannabis and alcohol – a close friendship
Raphael Mechoulam, Linda A. Parker
2003· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
66
citations
affno abstractunlabeled
Reassessing the Th2 cytokine basis of asthma
Paul M. O’Byrne, Mark D. Inman, E Ädelroth
2004· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
61
citations
affno abstractunlabeled
Fuzzy pharmacology: theory and applications
Beth Sproule, Claudio A. Naranjo, İ.B. Türkşen
2002· review· en· Trends in Pharmacological Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affno abstractunlabeled
Exercise Pills: At the Starting Line
Shunchang Li, Ismail Laher
2015· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
56
citations
affno abstractunlabeled
GIP or not GIP? That is the question
Timothy J. Kieffer
2003· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
53
citations
afffundno abstractunlabeled
New Frontiers in Lp(a)-Targeted Therapies
Matthew J. Borrelli, Amer Youssef, Michael B. Boffa, Marlys L. Koschinsky
2019· review· en· Trends in Pharmacological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
52
citations
affno abstractunlabeled
DNA microarrays in neuropsychopharmacology
Éric Marcotte, Lalit K. Srivastava, Rémi Quirion
2001· review· en· Trends in Pharmacological Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
40
citations

How this was built: Screen · Findings · About