{"id":"W4410216540","doi":"10.1007/978-3-031-87663-9_34","title":"Optimizing Granularity for Enhanced Handwriting Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Granularity; Handwriting; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002872093,0.0009365299,0.0007983388,0.0008694893,0.0003883277,0.001209607,0.0009495572,0.0005544653,0.008404779],"category_scores_gemma":[0.001838765,0.0004473156,0.0004462796,0.001117202,0.0002429981,0.001215432,0.001418902,0.0008248173,0.002515563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004458138,"about_ca_system_score_gemma":0.0006407013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001831934,"about_ca_topic_score_gemma":0.003740723,"domain_scores_codex":[0.9994795,0.00004958844,0.00004677135,0.0001381219,0.0002014025,0.00008462319],"domain_scores_gemma":[0.9989881,0.0003682021,0.00008177096,0.0003189006,0.0001867161,0.0000562814],"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":[0.0006544923,0.0001239737,0.0008558193,0.0001597332,0.00003787396,0.0001212826,0.00006906059,0.0254556,0.2014044,0.003364334,0.005130904,0.7626225],"study_design_scores_gemma":[0.00007143466,0.0002605802,0.002318485,0.00003500351,0.00005699321,0.0002872302,0.000086439,0.7698295,0.2060915,0.01007351,0.01085668,0.00003271247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04639331,0.000683766,0.9386641,0.00008314776,0.000113081,0.00005558473,0.0003178254,0.01009555,0.003593653],"genre_scores_gemma":[0.3283764,0.0003228823,0.6615674,0.0001195289,0.00007680333,0.00007871822,0.0008095382,0.001216454,0.007432429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008404779,"threshold_uncertainty_score":0.02811682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610035009770196,"score_gpt":0.270233114205087,"score_spread":0.2541327641073851,"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."}}