{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003146787,0.00006797494,0.00009947626,0.0001089775,0.000202904,0.00006398401,0.0006454104,0.00001872905,0.0008120124],"category_scores_gemma":[0.0001035359,0.00006136834,0.00006388394,0.0002031736,0.00001032219,0.0001463751,0.0002583386,0.00007460462,0.00001875651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002912923,"about_ca_system_score_gemma":0.00002592505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005697296,"about_ca_topic_score_gemma":0.000001320241,"domain_scores_codex":[0.9992751,0.00005466604,0.0001286605,0.0002050902,0.0001886759,0.0001477539],"domain_scores_gemma":[0.9992289,0.0003779954,0.00004246507,0.0002770606,0.00004053121,0.00003307305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008292262,0.0002020483,0.00001374423,0.00007842025,0.00003507899,0.00001094432,0.0003260194,0.00006647472,0.00436178,0.1942084,0.1207734,0.6799154],"study_design_scores_gemma":[0.0005612751,0.0006298061,0.0001537016,0.00003973805,0.00003830376,0.00007753637,0.0002203546,0.3659129,0.2058415,0.2097357,0.2157996,0.0009894791],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002207471,0.00003633502,0.9880546,0.001266266,0.0001012858,0.0003253611,0.000007467754,0.0006792516,0.00930869],"genre_scores_gemma":[0.2342782,0.000001873131,0.7617117,0.001422803,0.0000241346,0.001401361,0.000002219495,0.000008536492,0.001149124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.678926,"threshold_uncertainty_score":0.8890967,"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."}}