{"id":"W1456593600","doi":"10.63317/3iqewee2q6mv","title":"Detecting Reduplication in Videos of American Sign Language","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reduplication; Computer science; American Sign Language; Sign language; Artificial intelligence; Robustness (evolution); A priori and a posteriori; Natural language processing; Inflection; Speech recognition; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003547308,0.00003904585,0.0000853399,0.00008441681,0.00001091446,0.00001100314,0.0001560498,0.00001528799,0.000007307771],"category_scores_gemma":[0.00004600297,0.00003314793,0.0000170715,0.000410292,0.000012272,0.0002180537,0.00003409076,0.00003888923,0.00004235274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001584677,"about_ca_system_score_gemma":0.000009130741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002781663,"about_ca_topic_score_gemma":0.00004112396,"domain_scores_codex":[0.9994825,0.00005823715,0.0001483582,0.00009154892,0.00009430679,0.0001250601],"domain_scores_gemma":[0.9995692,0.00008684219,0.00008707168,0.0001938878,0.00002494907,0.0000379862],"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":[0.000002531266,0.0001219729,0.05347868,0.00002367143,0.000007057139,0.000001068237,0.01256659,0.000009868785,0.05433394,0.00359708,0.0000906903,0.8757669],"study_design_scores_gemma":[0.0007078488,0.0002234367,0.3618695,0.0001229751,0.000006803501,0.00005421079,0.005199461,0.009661986,0.6185198,0.0003217756,0.002774299,0.0005379826],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5405008,0.0001007927,0.4495996,0.0001584144,0.00008757401,0.0001239866,2.998315e-7,0.00007640947,0.009352172],"genre_scores_gemma":[0.9851164,0.000001025789,0.0146955,0.00006013288,0.00004362231,0.00001211415,3.11437e-7,0.000002459007,0.00006843435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8752289,"threshold_uncertainty_score":0.1351734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154307968808873,"score_gpt":0.2749224633391233,"score_spread":0.2594916664582361,"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."}}