{"id":"W4392298966","doi":"10.21203/rs.3.rs-3994630/v1","title":"TongueTransUNet: Toward Effective Tongue Contour Segmentation Using Small Dataset","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Linguistics and Cultural Studies","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Segmentation; Tongue; Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision; Linguistics; Philosophy","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007557375,0.0003215718,0.0003803215,0.0002026025,0.0006500223,0.001216364,0.0003386213,0.0001477781,0.0009065756],"category_scores_gemma":[0.0002109428,0.0002447013,0.000167147,0.00007147997,0.0003469908,0.00004394803,0.001223416,0.001373659,0.0002528767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003390751,"about_ca_system_score_gemma":0.0001316215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094388,"about_ca_topic_score_gemma":0.006145544,"domain_scores_codex":[0.9975392,0.0002664718,0.0003104828,0.000640445,0.0007023801,0.0005410828],"domain_scores_gemma":[0.9983867,0.0002399116,0.00006729376,0.000332437,0.0008578227,0.0001158173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003833179,0.0006115125,0.000505665,0.02808632,0.003567692,0.001022948,0.4054374,0.0006351625,0.001398194,0.2968872,0.2321823,0.0292823],"study_design_scores_gemma":[0.001077973,0.0007414687,0.0007873902,0.005547132,0.0005560034,0.000007484102,0.07257962,0.00289274,0.00124686,0.07407834,0.8386891,0.001795915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.671459,0.04675507,0.0001650968,0.003508108,0.01508881,0.01362102,0.1047884,0.0007759327,0.1438385],"genre_scores_gemma":[0.9862939,0.000367639,0.0001157548,0.00005638182,0.004656361,0.0003752144,0.005566682,0.00006345563,0.002504598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6065068,"threshold_uncertainty_score":0.9998205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2350743319204666,"score_gpt":0.4234799237801962,"score_spread":0.1884055918597296,"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."}}