{"id":"W2797542451","doi":"10.1039/c7lc01236e","title":"Handheld skin printer: <i>in situ</i> formation of planar biomaterials and tissues","year":2018,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Health Sciences Centre; Sunnybrook Health Science Centre; University of New Brunswick; University of Toronto; Toronto Public Health","funders":"National Institute of General Medical Sciences; Institute of Musculoskeletal Health and Arthritis; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Grand Challenges Canada; Canada Foundation for Innovation","keywords":"In situ; Biomaterial; Biomedical engineering; Planar; Materials science; Chemistry; Medicine; Computer science; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002601024,0.00006188729,0.0001082454,0.0001057203,0.00001292781,0.00001926158,0.00008751958,0.00005436066,0.00005264428],"category_scores_gemma":[0.00007015527,0.00005373847,0.00001000445,0.00009744581,0.00006433153,0.00005141669,0.0000284577,0.00004022712,0.00006922073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633889,"about_ca_system_score_gemma":0.000003569172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002055292,"about_ca_topic_score_gemma":0.00005432869,"domain_scores_codex":[0.9994518,0.00002394501,0.0001646159,0.00007807448,0.0001352125,0.0001464112],"domain_scores_gemma":[0.9997578,0.00005531617,0.00001786196,0.0001131998,0.00001428444,0.00004159042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005235343,0.00003531184,0.001044234,0.0004361027,0.00001333746,0.00000565962,0.001454604,0.000003384331,0.9679438,0.001007469,0.00136001,0.02664372],"study_design_scores_gemma":[0.0002881229,0.0001029421,0.00950278,0.0001820212,0.000001301399,0.000002345625,0.00001951309,0.0007517272,0.9825056,0.0003558121,0.006222028,0.0000657971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923113,0.00004949823,0.0003403492,0.00009087546,0.0001162978,0.00009605492,0.0000061757,0.00005813275,0.006931326],"genre_scores_gemma":[0.9992166,0.00004656022,0.000576181,0.00002725101,0.00006739103,0.000005373847,0.000004320832,0.000009584296,0.00004671602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02657792,"threshold_uncertainty_score":0.2191391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689300318177165,"score_gpt":0.2750591649051796,"score_spread":0.2581661617234079,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}