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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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Process Optimization and Integration
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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
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

643 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
643 works in the cohort · of 4,299,418page 10 of 13

Labels cover 1 of 643 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 643 of 643 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
Distillation
Raj Barchha, Ajay K. Ray
2023· book-chapter· en· Elsevier eBooks· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Process and Process Variables
Seyed Ali Ashrafizadeh, Zhongchao Tan
2018· book-chapter· en· Mechanical engineering series· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Exergy and industrial ecology
İbrahim Dinçer, Marc A. Rosen
2020· book-chapter· en· Elsevier eBooks· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Parametric Study of a Novel Basket Impeller Column
Tomasz Safinski, Adesoji A. Adesina, Brent R. Young
2003· article· en· The Canadian Journal of Chemical Engineering· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Conclusions and Outlook
S.S.E.H. Elnashaie, Firoozeh Danafar, Hassan Hashemipour
2015· book-chapter· en· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
afffundunlabeled
Multiple Optima in Gasoline Blend Planning
Shefali Kulkarni-Thaker, Vladimir Mahalec
2013· article· en· Industrial & Engineering Chemistry Research· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Theoretical Process Dynamic Modeling
Sohrab Rohani, Yuanyi Wu
2017· book-chapter· en· Elsevier eBooks· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Inner City
2018· other· en· RADAR (Glasgow School of Art)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Analyzing Optimization Results
Alireza Ghadertootoonchi, Armaghan Solaimanian, Mehdi Davoudi, Moein Moeini‐Aghtaie
2024· book-chapter· en· SpringerBriefs in energy· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Sulphuric acid plant water saving options
R.J. Forzatti, I Putu Gede Surya Adi Natha, Louis D. le Roux, D.A. van den Berg
2014· article· en· Engineering
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About