{"id":"W2576977424","doi":"10.5539/ijel.v7n2p167","title":"Melodic Features of Cause and Result Clauses in Modern English in the Light of Experiments","year":2017,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Discourse Analysis and Cultural Communication","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentence; Intonation (linguistics); Linguistics; Feature (linguistics); Computer science; Realization (probability); Melody; Natural language processing; Adverbial; Coding (social sciences); Artificial intelligence; Speech recognition; Mathematics; Musical; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004090097,0.0001533537,0.0001630838,0.0003821411,0.0003142956,0.0006851507,0.0002375053,0.0003358435,0.00532884],"category_scores_gemma":[0.003287761,0.000120975,0.0001054919,0.0002891584,0.0005469812,0.0005496594,0.0004450481,0.0004055201,0.0003538889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288274,"about_ca_system_score_gemma":0.0000728692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348712,"about_ca_topic_score_gemma":0.001511319,"domain_scores_codex":[0.9997961,0.00007275578,0.00001663587,0.00003419491,0.0000574152,0.00002280363],"domain_scores_gemma":[0.9987518,0.0009140327,0.00007186181,0.00006550123,0.0001321938,0.00006465495],"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":[0.01033915,0.001418867,0.0492812,0.00123056,0.0001387853,0.002704919,0.07535547,0.001420447,0.7853487,0.02065247,0.003558117,0.04855137],"study_design_scores_gemma":[0.0008778153,0.004440534,0.8075117,0.0001340218,0.0002042364,0.003399579,0.03879385,0.009960668,0.09278893,0.01173102,0.02996222,0.0001954729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945122,0.0000964935,0.0003284584,0.00004272231,0.00001037093,0.00002102844,0.0001639772,0.00001139186,0.004813372],"genre_scores_gemma":[0.997503,0.00004948151,0.0005584637,0.00004149483,0.000006931222,0.00002293757,0.0002612392,0.00001899067,0.001537539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00532884,"threshold_uncertainty_score":0.01782674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450643441866668,"score_gpt":0.3881734641105547,"score_spread":0.343667029691888,"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."}}