{"id":"W1850778893","doi":"10.1007/978-3-540-24840-8_44","title":"Artificial Aging of Faces by Support Vector Machines","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Image warping; Feature (linguistics); Pattern recognition (psychology); Displacement (psychology); Computer vision; Face (sociological concept); Facial expression; Feature vector","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":[0.0005105488,0.0003996965,0.0005608076,0.0007555166,0.0001580316,0.0003121843,0.002067969,0.0001965721,0.0001587233],"category_scores_gemma":[0.00005027799,0.0003577683,0.0002018076,0.0006784808,0.0005424063,0.0004410692,0.0005823443,0.0004505039,0.00005843809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001455792,"about_ca_system_score_gemma":0.000466018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005920998,"about_ca_topic_score_gemma":0.00007572475,"domain_scores_codex":[0.9969812,0.00002410259,0.000579353,0.001062147,0.0008949273,0.0004582396],"domain_scores_gemma":[0.9984382,0.000157904,0.0003363773,0.0007241507,0.0002025815,0.0001407573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000305497,0.00006205638,0.00007245372,0.00007137959,0.00003160521,0.00004691272,0.0007139632,0.01174353,0.002981923,0.01277532,0.000071305,0.9714265],"study_design_scores_gemma":[0.0004586625,0.000407323,0.0001101359,0.0008165889,0.00005829219,0.00007381427,7.625217e-7,0.507845,0.08853897,0.3979749,0.001795808,0.001919703],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002245337,0.0002530277,0.9955893,0.00122564,0.0008372219,0.0001576297,0.0000219359,0.0001035575,0.001587143],"genre_scores_gemma":[0.7639768,0.0001090711,0.2327093,0.001761899,0.0004151175,0.000007903198,0.00003708944,0.00004994009,0.0009328834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9695068,"threshold_uncertainty_score":0.9998874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151473631204635,"score_gpt":0.2475859680552227,"score_spread":0.2324386049347592,"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."}}