{"id":"W4401911681","doi":"10.2139/ssrn.4932446","title":"Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Human Motion and Animation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Margin (machine learning); Point (geometry); Computer science; Uncertainty quantification; Artificial intelligence; Mathematics; Machine learning; Geometry","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009428909,0.0003306459,0.0002635686,0.0003367181,0.0001430074,0.0002394143,0.0002018057,0.0001611067,0.00004075081],"category_scores_gemma":[0.0000344587,0.0002802055,0.0001117891,0.0001827527,0.00001537938,0.00006057919,0.00006763557,0.004445602,0.0002235993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776783,"about_ca_system_score_gemma":0.0007159036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000234514,"about_ca_topic_score_gemma":0.0004518213,"domain_scores_codex":[0.99784,0.0001077189,0.0003631632,0.0003669071,0.000303485,0.001018675],"domain_scores_gemma":[0.9994056,0.00002301101,0.000120531,0.0002034546,0.000142882,0.0001045227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009135791,0.00001844684,0.0000114933,0.00006274954,0.0002394626,0.000004238422,0.000707317,0.9706356,0.001336296,0.01640867,0.0003508883,0.01013345],"study_design_scores_gemma":[0.0007562223,0.001505554,0.0004378881,0.001782556,0.0001893527,0.0002005074,0.003240948,0.8871972,0.001693171,0.09814449,0.003534762,0.001317349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8025339,0.001663572,0.1889874,0.001147661,0.001000647,0.0007348673,0.00001793434,0.000839475,0.003074591],"genre_scores_gemma":[0.9951939,0.0010671,0.0004796053,0.00003348885,0.0006431742,0.0000452731,0.00006116637,0.00009610307,0.002380218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.19266,"threshold_uncertainty_score":0.999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539390231292653,"score_gpt":0.2422823768724477,"score_spread":0.2268884745595212,"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."}}