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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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Ocular Surface and Contact Lens
Retraction
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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.

affaffiliation
fundfunder
venuejournal
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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.

1,485 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.
1,485 works in the cohort · of 4,299,418page 18 of 30

Labels cover 5 of 1,485 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 1,485 of 1,485 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.

venueno affunlabeled
Compliance Using Scleral Lenses
Daddi Fadel, Mindy Toabe
2018· article· en· Journal of Contact lens Research and Science· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Impression Cytology of the Lid Wiper Area
Lyndon Jones, Lakshman N. Subbaraman, Alex Müntz, Kevin van Doorn
2017· article· en· UWSpace (University of Waterloo)· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Development of a contact Lens risk survey
G. Lynn Mitchell, Kathryn Richdale, Dawn Y. Lam, Heidi Wagner, Beth T. Kinoshita, Aaron B. Zimmerman +2 more
2020· article· en· Contact Lens and Anterior Eye· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Ocular Impression-Taking—Which Material Is Best?
Jennifer M. Turner, Christine Purslow, Paul J. Murphy
2018· article· en· Eye & Contact Lens Science & Clinical Practice· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
venueno affno abstractunlabeled
10.1016/j.yoph.2015.04.058
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
3
citations
affno abstractunlabeled
When was the last time you fitted a soft lens?
Eef van der Worp, James S. Wolffsohn, Lyndon Jones
2020· article· en· Contact Lens and Anterior Eye· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
New Possibilities for Hyposecretory Dry Eye Treatment
S. V. Yanchenko, А. В. Малышев, G. R. Odilova, L. M. Petrosyan, M. Yu. Odilov
2023· article· en· Ophthalmology in Russia· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affvenueunlabeled
Dry eye disease
Rahul Sharma, Rookaya Mather
2014· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
My Tattoos Caused My Dry Eye?
Caitlin J Morrison, Joseph M Stamm
2016· article· en· Canadian journal of optometry/CJO. Canadian journal of optometry· Medicine
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About