{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006168593,0.0008138965,0.0009082119,0.001627284,0.0002443712,0.0007911017,0.0004879487,0.0007453301,0.001424601],"category_scores_gemma":[0.001242971,0.0001682862,0.0008725832,0.0007024127,0.0001524238,0.0008910992,0.0003850317,0.0004221759,0.001222671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000335718,"about_ca_system_score_gemma":0.0002900005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002375942,"about_ca_topic_score_gemma":0.00247723,"domain_scores_codex":[0.9995871,0.00004891028,0.00003263227,0.0001526149,0.00009501392,0.0000836957],"domain_scores_gemma":[0.9991379,0.0002254337,0.00008871544,0.00009729417,0.0003970274,0.00005364058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001474056,0.0003393208,0.03228261,0.0001849702,0.0002695991,0.0003004748,0.00009151483,0.01968062,0.09347894,0.0004962338,0.002978053,0.8484235],"study_design_scores_gemma":[0.00003544085,0.000501106,0.05606265,0.00004369908,0.0002753615,0.0007435298,0.00008607967,0.8705069,0.06874986,0.0008365465,0.002098508,0.00006031584],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5250641,0.002155672,0.4626898,0.0002897745,0.0005159201,0.0001718269,0.001458695,0.003809873,0.003844281],"genre_scores_gemma":[0.9154342,0.0005240687,0.07688441,0.00008431853,0.0001096829,0.00007552889,0.0009460478,0.00009347945,0.00584813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002375942,"threshold_uncertainty_score":0.004765809,"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."}}