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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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Stock Market Forecasting Methods
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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
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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.

943 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.
943 works in the cohort · of 4,299,418page 8 of 19

Labels cover 2 of 943 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 943 of 943 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
Stock Investment Decision Making: A Social Network Approach
Negar Koochakzadeh, Fatemeh Keshavarz, Atieh Sarraf, Ali Rahmani, Keivan Kianmehr, Mohammad Rifaie +2 more
2011· book-chapter· en· Studies in computational intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
A unified framework for financial commentary prediction
Ozan Ozyegen, Garima Malik, Mücahit Çevik, Kevin Ioi, Karim El Mokhtari
2024· article· en· Information Technology and Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
Empirical Finance
Shigeyuki Hamori
2020· article· en· Journal of risk and financial management· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
GAs and Financial Analysis
Matty Leus, Dwight Deugo, Franz Oppacher, R. Cattral
2001· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
EconSentGPT: A Universal Economic Sentiment Engine?
Aref Mahdavi Ardekani, Julie Bertz, Michael Dowling, Suwan Long
2023· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
aboutno affunlabeled
Ichimoku Cloud Forecasting Returns in the U.S.
Matthew Lutey, Dave Rayome
2022· article· en· GLOBAL BUSINESS & FINANCE REVIEW· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
Hybrid Deep Learning Architectures for Stock Market Prediction
Iren Valova, Natacha Gueorguieva, Thakkar Aayushi, Pulluri Nikitha, Hassan Mohamed
2023· article· en· Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Financial Risk Measurement for Financial Risk Management
Torben G. Andersen, Tim Bollerslev, Peter Christoffersen, Francis X. Diebold
2011· preprint· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About