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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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3D Printing in Biomedical Research
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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.

1,653 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.
1,653 works in the cohort · of 4,299,418page 1 of 34

Labels cover 0 of 1,653 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 1,653 of 1,653 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
Cell-laden microengineered gelatin methacrylate hydrogels
Jason W. Nichol, Sandeep T. Koshy, Hojae Bae, Chang Mo Hwang, Seda Yamanlar, Ali Khademhosseini
2010· article· en· Biomaterials· Engineering
machine prediction:candidate · noneconsensus · none
2,353
citations
afffundno abstractunlabeled
3D bioprinting for engineering complex tissues
Christian Mandrycky, Zongjie Wang, Keekyoung Kim, Deok‐Ho Kim
2015· review· en· Biotechnology Advances· Engineering
machine prediction:candidate · noneconsensus · none
1,726
citations
fundno affno abstractunlabeled
Common principles and best practices for engineering microbiomes
Christopher E. Lawson, William R. Harcombe, Roland Hatzenpichler, Stephen R. Lindemann, Frank E. Löffler, Michelle O’Malley +7 more
2019· review· en· Nature Reviews Microbiology· Engineering
machine prediction:candidate · noneconsensus · none
625
citations
affunlabeled
Microfabricated Biomaterials for Engineering 3D Tissues
Pınar Zorlutuna, Nasim Annabi, Gulden Camci‐Unal, Mehdi Nikkhah, Jae Min, Jason W. Nichol +4 more
2012· review· en· Advanced Materials· Engineering
machine prediction:candidate · noneconsensus · none
395
citations
afffundno abstractunlabeled
Human disease models in drug development
Anna Loewa, James J. Feng, Sarah Hedtrich
2023· review· en· Nature Reviews Bioengineering· Engineering
machine prediction:candidate · noneconsensus · none
293
citations
affunlabeled
3D Bioprinting in Skeletal Muscle Tissue Engineering
Serge Ostrovidov, Sahar Salehi, Marco Costantini, Kasinan Suthiwanich, Majid Ebrahimi, Ramin Banan Sadeghian +9 more
2019· review· en· Small· Engineering
machine prediction:candidate · noneconsensus · none
290
citations
fundno affno abstractunlabeled
Multiscale bioprinting of vascularized models
Amir K. Miri, Akbar Khalilpour, Berivan Çeçen, Sushila Maharjan, Su Ryon Shin, Ali Khademhosseini
2018· review· en· Biomaterials· Engineering
machine prediction:candidate · noneconsensus · none
263
citations
affunlabeled
Building Vascular Networks
Hojae Bae, Amey S. Puranik, Robert Gauvin, Faramarz Edalat, Brenda Carrillo‐Conde, Nicholas A. Peppas +1 more
2012· review· en· Science Translational Medicine· Engineering
machine prediction:candidate · noneconsensus · none
256
citations

How this was built: Screen · Findings · About