{"id":"W4220904106","doi":"10.18280/ts.390124","title":"A Rapid Advancing Image Segmentation Approach in Dental to Predict Cryst","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Curvature; Segmentation; Process (computing); Line (geometry); Computer science; Feature (linguistics); Artificial intelligence; Computer vision; Pixel; Categorization; Image segmentation; Voronoi diagram; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005377437,0.0001799793,0.0001819923,0.0004104118,0.0002517547,0.00009847851,0.000269937,0.00001916216,0.00293826],"category_scores_gemma":[0.00000708086,0.0002098083,0.0001311369,0.0006687751,0.00003147731,0.000412585,0.0001656013,0.0002355081,0.00004685023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983838,"about_ca_system_score_gemma":0.00002384415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000708108,"about_ca_topic_score_gemma":0.00002572142,"domain_scores_codex":[0.9979744,0.0001609509,0.000401895,0.0004078774,0.0006440927,0.0004107924],"domain_scores_gemma":[0.9995912,0.00003293341,0.00008596968,0.0001486129,0.00001643613,0.0001248756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001118576,0.003110621,0.3722016,0.0002447233,0.0002446234,0.001040858,0.007810454,0.01029646,0.520599,0.0004305338,0.02829997,0.05460253],"study_design_scores_gemma":[0.01716224,0.001802221,0.8225955,0.0001508565,0.0002347184,0.001410807,0.06377076,0.04535634,0.02773173,0.0003309312,0.01728867,0.00216518],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773582,0.0001626224,0.01597549,0.00005477461,0.0004025768,0.0007963087,0.000134259,0.0001026857,0.005013091],"genre_scores_gemma":[0.994233,0.000003332111,0.004437018,0.0004667535,0.0001132177,0.0004015674,0.0001918146,0.0000287475,0.0001245186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4928673,"threshold_uncertainty_score":0.9979732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056031599377675,"score_gpt":0.2447071141850691,"score_spread":0.2341467981912924,"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."}}