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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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scientometrics and bibliometrics research
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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.

2,100 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.
2,100 works in the cohort · of 4,299,418page 11 of 42

Labels cover 193 of 2,100 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 2,100 of 2,100 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 abstractgemma · bibliometricsgpt · bibliometrics+metaresearchmodels split
Neophilia ranking of scientific journals
Mikko Packalén, Jay Bhattacharya
2016· article· en· Scientometrics· Decision Sciences
machine prediction:candidate · bibliometricsconsensus · none
18
citations
affunlabeled
Journal Impact Factor: it will go away soon
Eleftherios P. Diamandis
2009· article· en· Clinical Chemistry and Laboratory Medicine (CCLM)· Decision Sciences
machine prediction:candidate · metaresearch+bibliometrics+insufficient_payloadconsensus · none
17
citations
affgemma · metaresearch+bibliometricsgpt · metaresearch+bibliometricsmodels agree
Characteristics of ‘mega’ peer-reviewers
Danielle B. Rice, Ba’ Pham, Justin Presseau, Andrea C. Tricco, David Moher
2022· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
17
citations
affaboutunlabeled
The role of collegiality in academic review, promotion, and tenure
DeDe Dawson, Esteban Morales, Erin C. McKiernan, Lesley A. Schimanski, Meredith T. Niles, Juan Pablo Alperín
2022· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
17
citations
aboutno affunlabeled
Knowledge Monopolies and Global Academic Publishing
Doménico Fiormonte, Ernesto Priego
2016· dataset· en· The Winnower· Decision Sciences
machine prediction:candidate · bibliometrics+scholarly_communicationconsensus · none
16
citations
affunlabeled
The democratization of scientific publishing
Clare Fiala, Eleftherios P. Diamandis
2019· review· en· BMC Medicine· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
16
citations
affunlabeled
Does the Level of Evidence of Paper Presentations at the Arthroscopy Association of North America Annual Meetings From 2006‐2010 Correlate With the 5‐Year Publication Rate or the Impact Factor of the Publishing Journal?
Jeffrey Kay, Muzammil Memon, Darren de, Andrew Duong, Nicole Simunovic, Olufemi R. Ayeni
2016· article· en· Arthroscopy The Journal of Arthroscopic and Related Surgery· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
16
citations
affaboutunlabeled
Federal Science eLibrary Pilot
Beverly Louise Brown, Cynthia Found, Merle McConnell
2007· article· en· The Electronic Library· Decision Sciences
machine prediction:candidate · scholarly_communicationconsensus · none
16
citations

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