{"id":"W4394714697","doi":"10.5220/0008919500002513","title":"Water-sensitive Gelatin Phantoms for Skin Water Content Imaging","year":2020,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gelatin; Water content; Medical imaging; Materials science; Environmental science; Computer science; Geology; Chemistry; Artificial intelligence","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":[0.001108711,0.0007991106,0.0002222722,0.0005060845,0.0001487231,0.0003454647,0.0004507938,0.0005199867,0.002541428],"category_scores_gemma":[0.001062498,0.0006124848,0.0001816566,0.0002398243,0.0003540206,0.0006943253,0.0004179826,0.0005866158,0.0006043032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002415182,"about_ca_system_score_gemma":0.0002201321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003870962,"about_ca_topic_score_gemma":0.0007706114,"domain_scores_codex":[0.9997227,0.000119024,0.00001384199,0.00005312352,0.00005518928,0.00003620682],"domain_scores_gemma":[0.9992968,0.0003582303,0.0001341038,0.00007459332,0.00005732548,0.00007892078],"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":[0.0001127859,0.00001659574,0.00004939338,0.00007736463,0.000004724253,0.0000466618,0.00002214246,0.0001679547,0.9973534,0.0003047704,0.0001069683,0.001737226],"study_design_scores_gemma":[0.00001749647,0.0002675542,0.0006552853,0.00002695035,0.00002401347,0.0005278844,0.00002430103,0.002352931,0.9914316,0.0001592381,0.004499372,0.00001333289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5169169,0.01824527,0.4489556,0.0009542597,0.0003124327,0.0006543652,0.0008645693,0.001597017,0.01149956],"genre_scores_gemma":[0.8333685,0.005722453,0.1516387,0.0003669637,0.00003901433,0.0004870756,0.000769885,0.0002873466,0.007320009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002541428,"threshold_uncertainty_score":0.008501887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941646436053334,"score_gpt":0.2023536492299786,"score_spread":0.1829371848694452,"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."}}