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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.

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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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AI in Service Interactions
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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.

638 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.
638 works in the cohort · of 4,299,418page 1 of 13

Labels cover 0 of 638 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 638 of 638 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
What Makes a Good Conversation?
Leigh Clark, Nadia Pantidi, Orla Cooney, Philip R. Doyle, Diego Garaialde, Justin Edwards +6 more
2019· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
443
citations
affunlabeled
AI-powered marketing: What, where, and how?
Abdul R. Ashraf, Waqar Nadeem
2024· article· en· International Journal of Information Management· Computer Science
machine prediction:candidate · noneconsensus · none
386
citations
affunlabeled
The State of Speech in HCI: Trends, Themes and Challenges
Leigh Clark, Philip R. Doyle, Diego Garaialde, Emer Gilmartin, Stephan Schlögl, Jens Edlund +5 more
2019· article· en· Interacting with Computers· Computer Science
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
189
citations
affunlabeled
Software Bots
Carlene Lebeuf, Margaret‐Anne Storey, Alexey Zagalsky
2017· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
158
citations
affunlabeled
Challenges in Chatbot Development
Ahmad Abdellatif, Diego Elias Costa, Khaled Badran, Rabe Abdalkareem, Emad Shihab
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
120
citations
affunlabeled
The dark side of artificial intelligence in services
Daniel Belanche, Russell W. Belk, Luis V. Casaló, Carlos Flavián
2024· article· en· Service Industries Journal· Computer Science
machine prediction:candidate · noneconsensus · none
109
citations
affunlabeled
Collaborating with technology-based autonomous agents
Isabella Seeber, Lena Waizenegger, Stefan Seidel, Stefan Morana, Izak Benbasat, Paul Benjamin Lowry
2020· article· en· Internet Research· Computer Science
machine prediction:candidate · noneconsensus · none
100
citations
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How this was built: Screen · Findings · About