{"id":"W6906714799","doi":"10.17632/4dv53x925y","title":"Third molar size predictive model (and associated dataset)","year":2021,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crown (dentistry); Molar; Computed tomography; Mandibular second molar","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.002538136,0.001243193,0.001563516,0.0002621274,0.0003770907,0.0005157951,0.005641054,0.00116853,0.0007726128],"category_scores_gemma":[0.007010875,0.001296636,0.00009333919,0.0007126718,0.0002964742,0.001565949,0.01257003,0.002336999,0.002447677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475614,"about_ca_system_score_gemma":0.001251787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321387,"about_ca_topic_score_gemma":0.002931245,"domain_scores_codex":[0.991704,0.0008115285,0.001059602,0.003331384,0.001874622,0.001218792],"domain_scores_gemma":[0.9857928,0.0007417586,0.0009225287,0.01172715,0.0002772971,0.0005384299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001173678,0.0004968142,0.00000573883,0.0001578721,0.001373711,0.0003702667,0.00002165626,0.0001846519,0.00008173685,0.000003323333,0.9971454,0.00004149903],"study_design_scores_gemma":[0.001436645,0.00006970079,0.00003136657,0.0003577378,0.001875114,0.00004469406,0.00006113588,0.03752077,0.0000110198,0.0001403284,0.9572772,0.001174272],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002065967,0.002027576,0.00004620375,0.000095631,0.000331263,0.0008113276,0.9962928,0.0002512803,0.0001232309],"genre_scores_gemma":[0.00001203756,0.00234517,0.0007086525,0.0005887474,0.0002712889,0.00008831669,0.995428,0.000255451,0.0003022935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03986815,"threshold_uncertainty_score":0.9999647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05795167647262952,"score_gpt":0.3108741144019651,"score_spread":0.2529224379293356,"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."}}