{"id":"W2262459148","doi":"","title":"Bimodal text and speech conversation during on-line lectures","year":2005,"lang":"en","type":"article","venue":"EdMedia: World Conference on Educational Media and Technology","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Conversation; Linguistics; Line (geometry); Computer science; Speech recognition; Communication; Psychology; Natural language processing; Philosophy; 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":[0.0004424259,0.0003592207,0.000441663,0.0004464207,0.0008281865,0.001368978,0.0004294084,0.0009564479,0.01084877],"category_scores_gemma":[0.006932875,0.0002394162,0.0001377203,0.0003193247,0.0003822864,0.0006493999,0.001181988,0.0005450516,0.001564612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000313772,"about_ca_system_score_gemma":0.0002427757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001960477,"about_ca_topic_score_gemma":0.003436497,"domain_scores_codex":[0.9994386,0.000211101,0.00001675529,0.0001146189,0.0001083849,0.0001104708],"domain_scores_gemma":[0.9969468,0.002326438,0.0001217853,0.00007508457,0.0002147656,0.0003152091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.03151456,0.0008566412,0.05475029,0.0009396226,0.0001604808,0.005126336,0.09765622,0.001582156,0.6869571,0.002095408,0.00871487,0.1096463],"study_design_scores_gemma":[0.0006967504,0.003242956,0.7777399,0.0006241944,0.0004143175,0.003344274,0.07968459,0.01888362,0.08795464,0.002741453,0.02439636,0.0002769592],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894766,0.0001447799,0.001224916,0.00005946409,0.00006022508,0.00006550501,0.0004034523,0.00009701771,0.008468106],"genre_scores_gemma":[0.995149,0.00005698522,0.0007166821,0.00005751992,0.00004173277,0.00006232651,0.0003256389,0.00006737753,0.003522728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084877,"threshold_uncertainty_score":0.03629273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0385748559874542,"score_gpt":0.263811362707412,"score_spread":0.2252365067199578,"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."}}