{"id":"W2489415525","doi":"10.1075/celcr.11.03new","title":"1. Asymmetry in English multi-verb sequences: A corpus-based approach","year":2008,"lang":"en","type":"book-chapter","venue":"Converging evidence in language and communication research","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Linguistics; Verb; Meaning (existential); Corpus linguistics; Computer science; Context (archaeology); Natural language processing; Construction grammar; Artificial intelligence; Psychology; History; Philosophy","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":[0.004781315,0.0001690091,0.0003213648,0.0007264639,0.0004146893,0.0001119534,0.0009923335,0.0004387024,0.000248601],"category_scores_gemma":[0.00597036,0.0001835258,0.00005139847,0.0003230853,0.001205984,0.000170365,0.0002708176,0.001390502,0.0000317431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004056682,"about_ca_system_score_gemma":0.0009547496,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202574,"about_ca_topic_score_gemma":0.006465451,"domain_scores_codex":[0.9969324,0.001116763,0.0004183861,0.000433564,0.0006852569,0.0004136081],"domain_scores_gemma":[0.9958192,0.002612962,0.0001575964,0.0007782191,0.0005083588,0.000123719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002166721,0.0004382526,0.02214685,0.0006108223,0.00006226514,0.0007039203,0.4880305,0.000149076,0.00007460881,0.4403809,0.004768234,0.0424179],"study_design_scores_gemma":[0.005529901,0.0002884951,0.005983756,0.01132659,0.00006109101,0.00002913127,0.09897038,0.01889663,0.0000840323,0.007547123,0.8486212,0.002661596],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0100607,0.09139099,0.0005885347,0.003594736,0.000499313,0.002621253,0.00002642985,0.0002999898,0.8909181],"genre_scores_gemma":[0.8785807,0.03914192,0.003675648,0.0004810447,0.0002079513,0.0001478425,0.00007747233,0.00004173579,0.07764573],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.86852,"threshold_uncertainty_score":0.9945533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2157644953343507,"score_gpt":0.4294573761885316,"score_spread":0.2136928808541809,"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."}}