{"id":"W4316039193","doi":"10.3390/s23020919","title":"Feasibility of Skin Water Content Imaging Using CMOS Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stage (stratigraphy); Edema; Water content; Biomedical engineering; Modality (human–computer interaction); Medicine; Materials science; Surgery; Computer science; Artificial intelligence; Biology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001265813,0.0001386584,0.0002707699,0.0001804387,0.0002913759,0.000006065855,0.0001297595,0.00006882397,0.0007338912],"category_scores_gemma":[0.0001133461,0.0001059576,0.0001313104,0.0001965381,0.00006510801,0.00007102531,0.000274639,0.0002367439,0.0006785769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009252778,"about_ca_system_score_gemma":0.0000325392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006067021,"about_ca_topic_score_gemma":0.00005823204,"domain_scores_codex":[0.9977109,0.0006387247,0.0005550837,0.0002951615,0.0002664208,0.0005337327],"domain_scores_gemma":[0.999055,0.00012274,0.000142979,0.0004305672,0.0001515484,0.00009718654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007106914,0.0007139849,0.6257157,0.005548058,0.0008194252,0.0002993202,0.06184072,0.01350843,0.2115652,0.006739704,0.06386387,0.008674966],"study_design_scores_gemma":[0.01024981,0.00009290619,0.5833309,0.001245875,0.0005592625,0.00001254428,0.0838419,0.1004163,0.03968067,0.005513448,0.1733383,0.00171806],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896053,0.00001530291,0.0001654784,0.001194531,0.0008823136,0.001045726,0.00001970421,0.0002431436,0.006828499],"genre_scores_gemma":[0.9858095,0.00001099737,0.0002423296,0.0003473817,0.00009124107,0.00001703392,0.00002050891,0.00002640695,0.01343461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1718845,"threshold_uncertainty_score":0.872196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2086791462296089,"score_gpt":0.4404766972628999,"score_spread":0.231797551033291,"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."}}