{"id":"W2111318059","doi":"10.1109/icdar.2009.251","title":"Pen Acoustic Emissions for Text and Gesture Recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Cursive; Speech recognition; Gesture; Similarity (geometry); Artificial intelligence; SIGNAL (programming language); Template matching; Pattern recognition (psychology); Image (mathematics)","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":[0.0004315585,0.0005442296,0.000446225,0.000602481,0.0001955555,0.0007483713,0.0005419966,0.0005710895,0.009327331],"category_scores_gemma":[0.001966516,0.0001855952,0.0002243834,0.0006287394,0.0002203619,0.0006851236,0.0004012843,0.0003910567,0.003757328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322447,"about_ca_system_score_gemma":0.0001732974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004186356,"about_ca_topic_score_gemma":0.0007041409,"domain_scores_codex":[0.9994509,0.0001172734,0.00002959257,0.00009078448,0.000282766,0.00002880208],"domain_scores_gemma":[0.9990364,0.0004885845,0.00006118978,0.0001432441,0.000235791,0.00003475179],"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":[0.0002419979,0.00006786588,0.001001814,0.0003206777,0.0000263719,0.0002095816,0.00007108493,0.003938365,0.2759557,0.00153726,0.002373255,0.714256],"study_design_scores_gemma":[0.00006067913,0.0004897669,0.009413817,0.0001413784,0.0001020933,0.001854123,0.0001721162,0.2086289,0.711728,0.003859792,0.06344721,0.0001021774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06057897,0.003568841,0.9168145,0.000286685,0.000236733,0.0001312567,0.0005824035,0.004556015,0.01324462],"genre_scores_gemma":[0.4470833,0.003105899,0.5165223,0.0002903414,0.0002403095,0.0002294776,0.001133013,0.0003950244,0.03100028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009327331,"threshold_uncertainty_score":0.03120297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02315801403522359,"score_gpt":0.2754912154432502,"score_spread":0.2523332014080266,"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."}}