{"id":"W4316924994","doi":"10.1109/ijcb54206.2022.10007945","title":"Kinematic Synthesis for 3D Signatures","year":2022,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Kinematics; Signature (topology); Computer science; Novelty; Artificial intelligence; Data mining; Pattern recognition (psychology); 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":[0.0005284449,0.0007049063,0.000532833,0.001261155,0.0003435852,0.0008389597,0.0007157091,0.0006814006,0.004123081],"category_scores_gemma":[0.002001707,0.0004773505,0.001012873,0.0008407507,0.0005625857,0.0008278559,0.000878774,0.0005761561,0.001941158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004873902,"about_ca_system_score_gemma":0.0006968925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001272988,"about_ca_topic_score_gemma":0.001199971,"domain_scores_codex":[0.9992616,0.0001010611,0.00004765907,0.0001607071,0.0003899144,0.00003907533],"domain_scores_gemma":[0.9992268,0.0002099633,0.0001319287,0.0002135745,0.0001861326,0.00003151994],"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":[0.0001650556,0.00006124774,0.001492665,0.0003417047,0.00006993498,0.0002234967,0.000259752,0.3864664,0.07251234,0.06076616,0.002011575,0.4756298],"study_design_scores_gemma":[0.00001959104,0.0001319322,0.0007105633,0.00005046025,0.00002253547,0.0004248112,0.00008458274,0.9143292,0.04188542,0.01617697,0.02611227,0.00005164515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0026515,0.00004796554,0.9960527,0.00001847023,0.00002637264,0.00002075333,0.00005377832,0.000400096,0.0007282965],"genre_scores_gemma":[0.1713242,0.0003086644,0.823882,0.00005248301,0.00004450957,0.0001146739,0.0006705759,0.0002984318,0.003304427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004123081,"threshold_uncertainty_score":0.01379311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608110875431436,"score_gpt":0.2511296400161193,"score_spread":0.235048531261805,"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."}}