{"id":"W2907177214","doi":"10.4000/books.aaccademia.4661","title":"A Markovian Kernel-based Approach for itaLIan Speech acT labEliNg","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Utterance; Computer science; Task (project management); Artificial intelligence; Kernel (algebra); Context (archaeology); Support vector machine; Hidden Markov model; Natural language processing; Speech recognition; Markov process; Feature (linguistics); Linguistics; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004335925,0.0005542578,0.0005967549,0.0003293771,0.0003249676,0.0001899766,0.002794519,0.00114701,0.0000173744],"category_scores_gemma":[0.00003375244,0.0006217584,0.000400787,0.00002846106,0.0001977046,0.0002802491,0.0006483617,0.0006352322,0.00003371348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002923615,"about_ca_system_score_gemma":0.0003859239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001504757,"about_ca_topic_score_gemma":0.00001368859,"domain_scores_codex":[0.9973586,0.00006396088,0.00033054,0.001154996,0.0004711951,0.0006207684],"domain_scores_gemma":[0.9975299,0.0001897104,0.0004308982,0.001237297,0.0002705319,0.0003416436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006471436,0.00007964253,0.00003706729,0.001072512,0.0007588725,0.0003594128,0.001330778,0.00006904421,0.0003281759,0.8606228,0.1058204,0.02887426],"study_design_scores_gemma":[0.00145167,0.0001498682,0.000002940808,0.0002141472,0.0001341743,0.00001904344,0.00002678928,0.006377762,0.00132534,0.001820925,0.9875766,0.0009007545],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002247511,0.00008383155,0.1576655,0.00003784192,0.0004800649,0.001100823,0.0001333462,0.000400567,0.8400756],"genre_scores_gemma":[0.001542752,0.00001012569,0.06782011,0.0002770175,0.0006929354,0.000006189015,0.0001315209,0.00008116525,0.9294382],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8817562,"threshold_uncertainty_score":0.9996234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03538561528486382,"score_gpt":0.2231755777540221,"score_spread":0.1877899624691582,"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."}}