{"id":"W4285274205","doi":"10.54364/aaiml.2022.1126","title":"Transfer Learning to Detect Age From Handwriting","year":2022,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence and Machine Learning","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Handwriting; Computer science; Artificial intelligence; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Natural language processing; Speech recognition; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007311525,0.0001871068,0.0002454248,0.0003108803,0.000634653,0.0001808673,0.0005193425,0.00003913797,0.0001640878],"category_scores_gemma":[0.000237771,0.0002088963,0.00005546055,0.0007315232,0.00004977165,0.0008175897,0.0004189392,0.0009818228,0.00002681986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000562076,"about_ca_system_score_gemma":0.00001959302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003691436,"about_ca_topic_score_gemma":0.0004145047,"domain_scores_codex":[0.9979395,0.0003481587,0.0004318719,0.0005982032,0.0002971599,0.0003850577],"domain_scores_gemma":[0.9992728,0.0003607513,0.0000575629,0.0001740047,0.00003234025,0.000102497],"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":[0.00002345318,0.00002090909,0.0005425308,0.000006557454,0.000003466973,0.0000705817,0.002425201,0.03141263,0.005598285,0.006158504,0.000001062422,0.9537368],"study_design_scores_gemma":[0.0002295088,0.001900583,0.0002657167,0.0002441006,0.00001904058,0.00007208205,0.006670971,0.3093504,0.2845522,0.2613286,0.1337534,0.001613402],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08347118,0.001896252,0.9126224,0.0004044937,0.0001620197,0.0002214989,0.000004023991,0.0003808636,0.0008372585],"genre_scores_gemma":[0.9690713,0.0005274731,0.02963337,0.0004755229,0.00006019374,0.0001222965,0.00001191237,0.0000193226,0.00007860666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9521234,"threshold_uncertainty_score":0.8518543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940891262589089,"score_gpt":0.295819454422322,"score_spread":0.2664105417964311,"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."}}