{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004890463,0.0006978921,0.0004811399,0.001164702,0.0001969318,0.0003714931,0.0004630156,0.0003977593,0.00247393],"category_scores_gemma":[0.001543868,0.0001401988,0.0003608538,0.0005908191,0.0001597091,0.0006550536,0.0004580904,0.0004297188,0.002258331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000363219,"about_ca_system_score_gemma":0.0003745449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863613,"about_ca_topic_score_gemma":0.002765382,"domain_scores_codex":[0.9996955,0.00003782327,0.00002906046,0.0001129358,0.00007662884,0.00004807219],"domain_scores_gemma":[0.9992074,0.0001747536,0.0001032055,0.00009473702,0.0003695158,0.00005045851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000445842,0.000271463,0.01175258,0.00009930752,0.00006586759,0.0002616145,0.00008479095,0.01129257,0.02972581,0.000309779,0.005710218,0.9399801],"study_design_scores_gemma":[0.00004820649,0.000657937,0.03598816,0.00005104314,0.00009826651,0.0006258325,0.0001638529,0.8989195,0.05297658,0.002240174,0.008187577,0.0000429004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6826283,0.00256527,0.2864677,0.0003983199,0.000798755,0.0004267242,0.003060238,0.009435116,0.01421953],"genre_scores_gemma":[0.9409598,0.000375279,0.04574956,0.0001241168,0.000151759,0.0001143181,0.001857567,0.0000725268,0.01059517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002863613,"threshold_uncertainty_score":0.008276105,"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."}}