{"id":"W2958708713","doi":"10.48550/arxiv.1906.11957","title":"Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Leverage (statistics); Probabilistic logic; Autoencoder; Artificial intelligence; Computer science; Voxel; Missing data; Machine learning; Deep learning","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"],"consensus_categories":[],"category_scores_codex":[0.0002708705,0.0002318987,0.000470198,0.0004790511,0.0001028811,0.00003936878,0.0003203567,0.0002337947,0.0002298412],"category_scores_gemma":[0.00007707152,0.0003046602,0.000540292,0.0002862309,0.0001471945,0.0002617606,0.0002625518,0.0003313418,0.00004249279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001186068,"about_ca_system_score_gemma":0.0001354789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008511214,"about_ca_topic_score_gemma":0.000007094271,"domain_scores_codex":[0.998645,0.00009345255,0.0003001417,0.0006260191,0.0001042024,0.0002312027],"domain_scores_gemma":[0.9981534,0.0006880061,0.0005283529,0.0003530027,0.0002111522,0.00006607724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007925205,0.0001815259,0.7888955,0.0004719745,0.0009404729,0.0001346417,0.0002363757,0.1287733,0.0002913201,0.0755604,0.002733947,0.0009880356],"study_design_scores_gemma":[0.001796722,0.00009426982,0.5357058,0.0008401378,0.0005301693,0.00006213145,0.001263109,0.4293269,0.000370376,0.02845231,0.0005331326,0.001024905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.857726,0.00003792709,0.1369088,0.000005525631,0.002300175,0.0003221722,0.0005750503,0.00005612721,0.002068152],"genre_scores_gemma":[0.9985112,0.00001479106,0.000408972,0.00002485874,0.00009835559,0.000001369923,0.0004445882,0.0000238375,0.0004720164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3005536,"threshold_uncertainty_score":0.9999406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08540001305531153,"score_gpt":0.2165384571193842,"score_spread":0.1311384440640726,"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."}}