{"id":"W4295011752","doi":"10.1016/j.patrec.2022.08.016","title":"Novel features to detect gender from handwritten documents","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Handwriting; Computer science; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Task (project management); Similarity (geometry); Data set; Handwriting recognition; Speech recognition; Natural language processing; Feature extraction; Image (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":[0.0002690179,0.0006031438,0.000547281,0.001703231,0.0003085295,0.0005021651,0.0004788298,0.0004716438,0.00332184],"category_scores_gemma":[0.0006992592,0.0001683539,0.0004108057,0.0009996598,0.0001558879,0.0006503664,0.0004511734,0.0004032399,0.002181828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002509163,"about_ca_system_score_gemma":0.0003539599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001339621,"about_ca_topic_score_gemma":0.003325637,"domain_scores_codex":[0.999688,0.00002193042,0.00002676471,0.00006781679,0.0001364216,0.00005902649],"domain_scores_gemma":[0.9993373,0.0001046881,0.00009746562,0.00006718202,0.0003255724,0.00006785353],"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":[0.0007733439,0.0002692931,0.007280568,0.00016245,0.00006368334,0.000377382,0.00006458018,0.001276162,0.3903019,0.0006783237,0.009274164,0.5894782],"study_design_scores_gemma":[0.0002432677,0.001677575,0.1697253,0.0001610332,0.0003983707,0.006793153,0.0004546058,0.2797286,0.4759539,0.002573624,0.06206984,0.0002207818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4567018,0.004572665,0.5125433,0.0005839752,0.00173432,0.0003989264,0.006729438,0.005674813,0.01106074],"genre_scores_gemma":[0.7427989,0.00129138,0.2226692,0.000361912,0.000642871,0.0002939152,0.006677479,0.0003524995,0.02491182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00332184,"threshold_uncertainty_score":0.01111263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266448539025716,"score_gpt":0.2527431218860396,"score_spread":0.226098267983468,"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."}}