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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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Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena
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Study design
Label agreement
Label status

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

115 results · 1 filter active ·
Results by year
20012025
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Machine labels · sparse coverage
Evidence
Language
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Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
115 works in the cohort · of 4,299,418page 2 of 3

Labels cover 0 of 115 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 115 of 115 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
Multiple Proton Implantations into Silicon: A Combined EBIC and SRP Study
Stefan Kirnstötter, Martin Faccinelli, Moriz Jelinek, Werner Schustereder, Johannes G. Laven, Hans Schulze +1 more
2013· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Mold Filling Simulation of Semi-Solid Magnesium Alloys
F. Ilinca, Jean François Hétu, F. Ajersch, Jean François Moisan
2008· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Texture Control in Manufacturing of ULSI Devices
K. J. Kozaczek
2005· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Materials Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Study of Durable Strength of Steel Mining and Metallurgical Equipment
В. Д. Макаренко, S.Yu. Maksimov, Yu. V. Маkarenko, Olena S. Panchenko
2022· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Martensite Fraction Determination Using Cooling Curve Analysis
Diana Marcano, Patricio F. Méndez, John W. Gibbs, Th. Kannengießer
2011· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
High Performance Unalloyed Ductile Cast Iron with Multiphase Structure
Wentao Zhou, Chen Yang, Xi Xi Cui, Zhong Liang, Xuan Wang, Derek O. Northwood +1 more
2019· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Ordering in Highly Supersaturated Alpha-Fe-C
Chad W. Sinclair, Michel Perez
2011· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Materials Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Theory and Technology of Semisolid Metal Molding
Frank Czerwiński
2008· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Bistable Defects as the Cause for NBTI and RTN
Wolfgang Goes, F. Schanovsky, H. Reisinger, B. Kaczer, Tibor Grasser
2011· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Fluid Flow Investigation of Die Cast Tensile Test Bars
Martin Forté, D. Bouchard, André B. Charette
2006· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Three-Step Room Temperature Wet Cleaning Process for Silicon Substrate
Rui Hasebe, Akinobu Teramoto, Tomoyuki Suwa, Rihito Kuroda, Shigetoshi Sugawa, Tadahiro Ohmi
2009· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A Molecular Tool for Carbon Transfer in Mechanosynthesis
K. Eric Drexler
2007· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Materials Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
SnO<sub>2</sub> Nanostructures Synthesized on Co Substrates
Hyoun Woo Kim, S.H. Shim, Hae Jin Hwang, Jae Hyun Shim, Nam Hee Cho, Mi Kyoung Park +6 more
2007· article· en· Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena· Engineering
machine prediction:candidate · noneconsensus · none
0
citations

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