{"id":"W4246185707","doi":"10.4172/plastic-surgery.1000471","title":"'Optimum mobility' facelift. Part 1 – the theory","year":2006,"lang":"en","type":"article","venue":"Plastic Surgery","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Mathematical economics; Mathematics","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005649247,0.0001446692,0.0001915719,0.0001020763,0.0001814527,0.0001477629,0.0001441706,0.00004205764,0.001058928],"category_scores_gemma":[0.0002958628,0.0001018695,0.0001333937,0.0001538287,0.0001145483,0.0003566046,0.00003777988,0.00008080675,0.001096804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001255203,"about_ca_system_score_gemma":0.00001792182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005251655,"about_ca_topic_score_gemma":0.0001315277,"domain_scores_codex":[0.999092,0.000007861453,0.000288122,0.0001821793,0.00009929783,0.0003305522],"domain_scores_gemma":[0.9968455,0.002790152,0.0001092701,0.0002222969,0.00002490109,0.000007902781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001405665,0.0001944103,0.7186261,0.0001254184,0.00006038312,0.0000161519,0.0000450017,0.002499057,0.00005175476,0.078816,0.1970859,0.002339156],"study_design_scores_gemma":[0.0001085001,9.461941e-7,0.3729576,0.00002308789,0.00004955497,0.000001302026,0.0001298506,0.0007139709,0.00002868836,0.007109611,0.6186497,0.0002271582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770442,0.0001230064,0.0002048047,0.0005053257,0.00386146,0.000071676,0.000007695852,0.0001641354,0.0180177],"genre_scores_gemma":[0.9966639,0.000005812615,0.000002606524,0.0005134828,0.002141918,0.00002614735,0.00004288443,0.00001865117,0.0005846227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4215638,"threshold_uncertainty_score":0.9998543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701176139209802,"score_gpt":0.1794308231239267,"score_spread":0.1624190617318287,"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."}}