{"id":"W2296829524","doi":"10.18869/acadpub.jsri.6.2.141","title":"Persian Handwriting Analysis Using Functional Principal Components","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Research of Iran","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Principal component analysis; Handwriting; Functional principal component analysis; Computer science; Functional data analysis; Pattern recognition (psychology); Artificial intelligence; Handwriting recognition; Representation (politics); Variation (astronomy); Principal (computer security); Object (grammar); Natural language processing; Speech recognition; Mathematics; Machine learning; Feature extraction","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":[0.0007178071,0.0008480485,0.0004288692,0.004321066,0.0004349849,0.001131443,0.000341825,0.0003165377,0.005935833],"category_scores_gemma":[0.002674695,0.000143511,0.0006989877,0.003422842,0.0004845211,0.0008243651,0.0003791931,0.0005183015,0.001861298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003434041,"about_ca_system_score_gemma":0.0007196249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004533213,"about_ca_topic_score_gemma":0.003165275,"domain_scores_codex":[0.9995068,0.00009662251,0.00004684691,0.0001520448,0.0001557242,0.00004196344],"domain_scores_gemma":[0.9991121,0.0002299388,0.00009664304,0.0001002108,0.000429983,0.00003121314],"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":[0.0002817241,0.00007593778,0.007481896,0.0003553131,0.0001527731,0.0004430939,0.0004008601,0.01485412,0.0255896,0.00723362,0.007857184,0.9352738],"study_design_scores_gemma":[0.00005940214,0.0004841477,0.1900035,0.0002729347,0.0003967709,0.003440882,0.002596521,0.60707,0.06930684,0.02822516,0.09788007,0.0002637364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1676495,0.002418104,0.798861,0.0005701668,0.0003681379,0.0003050888,0.002876234,0.003597695,0.0233541],"genre_scores_gemma":[0.6349856,0.001611643,0.3497854,0.00008879911,0.0001950244,0.0002476879,0.00320638,0.0002434796,0.009635932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005935833,"threshold_uncertainty_score":0.01985741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1965802296252985,"score_gpt":0.429603254404026,"score_spread":0.2330230247787275,"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."}}