{"id":"W3196423532","doi":"","title":"Извлечение серебряных штифтов из просвета корневого канала при повторном эндодонтическом вмешательстве","year":2012,"lang":"ru","type":"article","venue":"Clinical Dentistry (Russia)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Office of the Chief Medical Examiner","funders":"","keywords":"Computer science","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","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002680217,0.00146532,0.00217053,0.0003375471,0.0006534522,0.0002633467,0.002103717,0.00381667,0.005079648],"category_scores_gemma":[0.00136211,0.001567718,0.001401777,0.0009516894,0.001743646,0.001226226,0.0007881615,0.004701482,0.01071209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583422,"about_ca_system_score_gemma":0.0002653581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001036129,"about_ca_topic_score_gemma":0.00008344626,"domain_scores_codex":[0.9901886,0.0006409358,0.003584847,0.001607037,0.0009118149,0.003066796],"domain_scores_gemma":[0.9935559,0.001397931,0.000578451,0.002847059,0.0001540509,0.001466596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007403562,0.006067259,0.4564542,0.00347481,0.004552062,0.003152135,0.001148019,0.0007020387,0.001341132,0.2260555,0.1721345,0.124178],"study_design_scores_gemma":[0.005398353,0.001012635,0.7785054,0.001160629,0.001628016,0.001200579,0.001814662,0.004340055,0.001107806,0.02460423,0.1745455,0.0046822],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7517712,0.04109826,0.004088499,0.001784203,0.03223847,0.001159092,0.000560426,0.002863456,0.1644365],"genre_scores_gemma":[0.9748875,0.004403433,0.002247232,0.0004527662,0.004271993,0.00009252967,0.0002039389,0.0002578597,0.01318278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3220512,"threshold_uncertainty_score":0.9998096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06614917701997491,"score_gpt":0.3561112144758138,"score_spread":0.2899620374558389,"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."}}