{"id":"W4319073283","doi":"10.1016/j.patrec.2023.02.001","title":"Age detection from handwriting using different feature classification models","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Handwriting; Pattern recognition (psychology); Artificial intelligence; Computer science; Artificial neural network; Field (mathematics); Feature (linguistics); Feature extraction; Similarity (geometry); Handwriting recognition; Set (abstract data type); Machine learning; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000253065,0.000282483,0.0002454413,0.0004861751,0.0002912197,0.0004434161,0.0004460356,0.0001672799,0.00003854176],"category_scores_gemma":[0.00003346335,0.0002943497,0.0001518829,0.0006409975,0.00004606539,0.001095591,0.000159767,0.0003449964,0.0003297255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597772,"about_ca_system_score_gemma":0.00001203475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001344853,"about_ca_topic_score_gemma":0.00004610614,"domain_scores_codex":[0.9978153,0.0002219728,0.0003646464,0.0007237309,0.0004369507,0.0004373588],"domain_scores_gemma":[0.9989287,0.0001682084,0.000225815,0.0004466937,0.0001093601,0.0001211948],"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":[0.000004935438,0.00003235544,0.0002667519,0.00002395008,0.0000297261,0.0000526485,0.0003535631,0.00004190927,0.3249087,0.000007859072,0.0007065689,0.6735711],"study_design_scores_gemma":[0.0007805509,0.00004788089,0.009819778,0.0003717264,0.00004959503,0.00003583799,0.000157352,0.7533559,0.220965,0.01342616,0.0001647904,0.0008255324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4261492,0.000008304228,0.5706748,0.00134208,0.0002873316,0.0002306828,0.0000420268,0.001182187,0.00008343434],"genre_scores_gemma":[0.9847526,0.00004110544,0.01160428,0.002614295,0.0003313952,0.00015529,0.0004414158,0.00004223927,0.00001738587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.753314,"threshold_uncertainty_score":0.9999509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07734622065577988,"score_gpt":0.2713761186785971,"score_spread":0.1940298980228172,"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."}}