{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002977429,0.0003270741,0.0001830085,0.0002348773,0.0001355462,0.0003783613,0.0004575001,0.0005373227,0.0008548053],"category_scores_gemma":[0.0008195813,0.0001911255,0.0003207031,0.0001672711,0.000220612,0.0005860804,0.0002181911,0.0001604084,0.0002884598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677849,"about_ca_system_score_gemma":0.0002939179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006944193,"about_ca_topic_score_gemma":0.001033177,"domain_scores_codex":[0.9997081,0.00004537637,0.0000100089,0.00006522506,0.0001453783,0.00002586499],"domain_scores_gemma":[0.9996754,0.0001373876,0.00004410473,0.00002206837,0.0001091591,0.00001185935],"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":[0.0001989607,0.0000829815,0.005105779,0.0002586154,0.00002680783,0.0001853192,0.00008411835,0.008885355,0.9449928,0.00160792,0.0003407508,0.03823051],"study_design_scores_gemma":[0.00002361144,0.0008439296,0.007030323,0.00004348113,0.0000799202,0.0004202971,0.000191642,0.2466101,0.7396884,0.001047352,0.003963842,0.00005713148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6109791,0.002198762,0.3781471,0.0005535943,0.000252256,0.000162474,0.0003405428,0.0006199466,0.006746253],"genre_scores_gemma":[0.9218261,0.0008339841,0.07588687,0.0001037085,0.00003093731,0.00004645888,0.00006626689,0.00001484945,0.001190801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008548053,"threshold_uncertainty_score":0.002859652,"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."}}