{"id":"W4415318477","doi":"10.1109/synasc69064.2025.00010","title":"Well-Conditioned Polynomial Representations for Mathematical Handwriting Recognition","year":2025,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Legendre polynomials; Representation (politics); Polynomial; Parameterized complexity; Degree (music); Chebyshev polynomials; Degree of a polynomial; Matrix polynomial","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":[0.0007820827,0.000587317,0.0005014622,0.0007632254,0.0002316127,0.001245324,0.0007758792,0.0005196096,0.00326502],"category_scores_gemma":[0.003975201,0.0002202295,0.0004435682,0.0008930341,0.0008841589,0.002081815,0.0006853875,0.001550755,0.00145239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006205406,"about_ca_system_score_gemma":0.0006021863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008078834,"about_ca_topic_score_gemma":0.00120375,"domain_scores_codex":[0.9993215,0.0001668465,0.00004407225,0.00009174451,0.0003156897,0.00006014577],"domain_scores_gemma":[0.998669,0.0005403688,0.0001656724,0.0002942959,0.0002631757,0.0000674304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001485712,0.00006096755,0.0002881266,0.0001861563,0.00002232344,0.0001123404,0.0001393073,0.277464,0.05682648,0.4266793,0.001572081,0.2365003],"study_design_scores_gemma":[0.000005156643,0.00004054335,0.000120963,0.00001876744,0.000004390059,0.00005319472,0.00002525322,0.9065132,0.01329647,0.07711144,0.002791195,0.00001952318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01328498,0.0001740754,0.9846264,0.00005459024,0.00001771179,0.0000154764,0.00004182411,0.000251466,0.001533639],"genre_scores_gemma":[0.4954364,0.00111946,0.4963719,0.00007464104,0.0001245432,0.00006692076,0.0003495763,0.0002473222,0.006209206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00326502,"threshold_uncertainty_score":0.01092261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323032912985805,"score_gpt":0.3076448314337529,"score_spread":0.2844145023038949,"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."}}