{"id":"W3109540593","doi":"10.1142/9789811226830_0004","title":"Could Sigma-Lognormal Modeling Help Teachers to Characterize the Kinematic Efficiency of Pupils’ Cursive Procedures of Handwriting?","year":2020,"lang":"en","type":"book-chapter","venue":"Series in machine perception and artificial intelligence","topic":"Cognitive and developmental aspects of mathematical skills","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Handwriting; Log-normal distribution; Sigma; Cursive; Kinematics; Six Sigma; Computer science; Mathematics; Statistics; Artificial intelligence; Engineering; Physics; Manufacturing engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004320389,0.0003345936,0.0006400443,0.0001438109,0.0001059535,0.00004104873,0.0002713465,0.0001442178,0.0005753092],"category_scores_gemma":[0.001347628,0.0002494304,0.000110678,0.0001273655,0.0003488162,0.0001117294,0.0002182362,0.0003867753,0.00001678356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003255987,"about_ca_system_score_gemma":0.0000664371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002633337,"about_ca_topic_score_gemma":0.0001797702,"domain_scores_codex":[0.9979435,0.00003877858,0.001058972,0.000355604,0.0003931323,0.0002100757],"domain_scores_gemma":[0.9988909,0.0003131593,0.0003141033,0.0001822345,0.0002078427,0.0000917322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007721981,0.0006178456,0.00005760865,0.004936509,0.0001519399,0.00002635119,0.06051757,0.0006086646,0.017182,0.7923707,0.0001567375,0.1226018],"study_design_scores_gemma":[0.0001036827,0.0006097754,0.00006059615,0.003928078,0.0001288242,0.00003026112,0.01158953,0.02269357,0.005390489,0.9545823,0.0001190117,0.0007638804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4654376,0.001053481,0.3617419,0.01027077,0.0006764749,0.008040269,0.0006701621,0.0003012196,0.1518081],"genre_scores_gemma":[0.9929813,0.0002844246,0.003986268,0.0002286744,0.00009764827,0.00003159556,0.00002643045,0.00004400277,0.002319683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5275437,"threshold_uncertainty_score":0.9999958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06551635445947622,"score_gpt":0.3146679817750499,"score_spread":0.2491516273155737,"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."}}