{"id":"W2293888193","doi":"","title":"LRRP SpeechWebs","year":2004,"lang":"en","type":"article","venue":"Conference on Communication Networks and Services Research","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Hyperlink; Architecture; The Internet; Speech analytics; World Wide Web; Web page; Speech synthesis; Speech recognition; Speech corpus","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":[0.001631292,0.001066952,0.0007232943,0.001751954,0.0009446695,0.004278911,0.002946703,0.001904567,0.1001569],"category_scores_gemma":[0.003620242,0.00101462,0.0008016717,0.001207917,0.0008206259,0.005533842,0.003859669,0.001965408,0.09522907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250617,"about_ca_system_score_gemma":0.001384597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005134216,"about_ca_topic_score_gemma":0.003665385,"domain_scores_codex":[0.9978235,0.0003964299,0.0001797907,0.0003396905,0.001010615,0.0002501007],"domain_scores_gemma":[0.997476,0.0004106401,0.0001317216,0.0009902557,0.0006651783,0.0003261148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001004334,0.0002455335,0.001333491,0.00073416,0.00007229037,0.001011338,0.00142494,0.00275748,0.02235398,0.06818126,0.4708523,0.4300289],"study_design_scores_gemma":[0.0000676711,0.00007569775,0.0005398605,0.00006751079,0.00002345282,0.0004634255,0.0001174779,0.01170335,0.01155341,0.007293749,0.9680405,0.000054004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.009363915,0.001067835,0.3291894,0.001407532,0.0007110401,0.0005243561,0.005502651,0.4401452,0.212088],"genre_scores_gemma":[0.1686473,0.002126888,0.2463046,0.003105668,0.0008124343,0.001156033,0.05004197,0.05510351,0.4727015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1001569,"threshold_uncertainty_score":0.3350581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0761207002343366,"score_gpt":0.3511730361322229,"score_spread":0.2750523358978864,"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."}}