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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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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.

13 results · 1 filter active ·
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20172021
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Categories
Machine labels · sparse coverage
Evidence
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Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
13 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 13 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 13 of 13 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.

affunlabeled
Artificial intelligence, machine learning and deep learning
Maria Pia Del Rosso, Silvia Liberata Ullo, Alessandro Sebastianelli, Dario Spiller, Erika Puglisi, Diego Di Martire +2 more
2021· book-chapter· en· IET eBooks· Engineering
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Artificial neural network
Maria Pia Del Rosso, Silvia Liberata Ullo, Alessandro Sebastianelli, Dario Spiller, Erika Puglisi, Filippo Biondi +1 more
2021· book-chapter· en· IET eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Convolutional neural networks
Silvia Liberata Ullo, Alessandro Sebastianelli, Maria Pia Del Rosso, Dario Spiller, Erika Puglisi, Artur Nowakowski +2 more
2021· book-chapter· en· IET eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
AALaaS/ELEaaS platforms
Rossitza Goleva, Māra Pudāne, Sintija Petroviča, Egons Lavendelis, Karl Kreiner, Mario Drobics +9 more
2017· book-chapter· en· IET eBooks· Engineering
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
How to develop your network with Python and Keras
Silvia Liberata Ullo, Maria Pia Del Rosso, Alessandro Sebastianelli, Erika Puglisi, Mario Luca Bernardi, Marta Cimitile
2021· book-chapter· en· IET eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A classification problem
Alessandro Sebastianelli, Maria Pia Del Rosso, Chiara Zarro, Diego Di Martire, Mariano Di Napoli, Dario Spiller +4 more
2021· book-chapter· en· IET eBooks· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A generation problem
Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo, Erika Puglisi, Filippo Biondi
2021· book-chapter· en· IET eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Biosensors in healthcare: an overview
R. Indrakumari, T. Poongodi, Fadi Al‐Turjman
2020· book-chapter· en· IET eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About