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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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Complex Network Analysis Techniques
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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
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venuejournal
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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.

1,618 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.
1,618 works in the cohort · of 4,299,418page 30 of 33

Labels cover 3 of 1,618 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 1,618 of 1,618 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
Trend Detection
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Background
Mosab Alfaqeeh, David B. Skillicorn
2024· book-chapter· en· Lecture notes in social networks· Physics and Astronomy
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Scaling Up
Mosab Alfaqeeh, David B. Skillicorn
2024· book-chapter· en· Lecture notes in social networks· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Network Induction
James Alexander Hughes, Sheridan Houghten, Michael P. Dubé, Daniel Ashlock, Joseph Alexander Brown, Wendy Ashlock +1 more
2024· book-chapter· en· Synthesis lectures on learning, networks, and algorithms· Physics and Astronomy
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
afffundno abstractunlabeled
Dynamics of Large-Scale Networks Following a Merger
John Clements, Babak Farzad, Henryk Fukś
2018· book-chapter· en· Lecture notes in social networks· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Spatiotemporal Network Topology Analysis
Khadige Abboud, Weihua Zhuang
2015· book-chapter· en· Springer briefs in electrical and computer engineering· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Online Bayesian Inference of Diffusion Networks
Shohreh Shaghaghian, Mark Coates
2017· preprint· en· IEEE Transactions on Signal and Information Processing over Networks· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Controllability of Complex Power Networks
Guo‐Hua Zhang, Zhen Li, Qiaoli Zhang
2017· article· en· Network and Communication Technologies· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Large-Scale Network
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
How Are Social Network Data Visualized?
2023· book-chapter· en· Cambridge University Press eBooks· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Networks and Culture
2023· book-chapter· en· Cambridge University Press eBooks· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Understanding alliance and opposition among violent groups
Qi‐Tai Zheng, David B. Skillicorn
2016· article· en· 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
afffundvenueunlabeled
Restricted Tweedie stochastic block models
節子 春間, Mu Zhu, Peijun Sang
2025· article· en· Canadian Journal of Statistics· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Art About Networks
2018· book-chapter· en· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Visually Mining Relational Data
Yves Chiricota, Guy Mélançon
2018· article· en· International Journal on Computer and Communications Networks Computational Intelligence and Data Analytics· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About