{"id":"W2408643148","doi":"10.1353/cjl.2015.0027","title":"Adverb extraction, specificity, and structural parallelism","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Linguistics / La revue canadienne de linguistique","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adverb; Linguistics; Parallelism (grammar); Natural language processing; Computer science; Applied linguistics; Cognitive linguistics; Artificial intelligence; Psychology; Philosophy; Cognition; Noun","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002102051,0.0002707021,0.0003936538,0.0003234239,0.0006433826,0.0005344411,0.0004835597,0.0001544745,0.000141687],"category_scores_gemma":[0.03447994,0.0002360844,0.0001027898,0.00008007764,0.000542501,0.00004447352,0.00002724339,0.0007600015,0.00000808623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008180052,"about_ca_system_score_gemma":0.002370238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2636759,"about_ca_topic_score_gemma":0.9182009,"domain_scores_codex":[0.9979792,0.0002184071,0.00074754,0.0002152345,0.0001658848,0.0006736851],"domain_scores_gemma":[0.9935758,0.00049756,0.0006395834,0.0003351682,0.003460029,0.001491872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002774663,0.000006713731,0.001768935,0.00004014616,0.00007691137,0.0009267658,0.08229391,0.0006477263,0.000002078561,0.9129235,0.001098935,0.0001866745],"study_design_scores_gemma":[0.0005505387,0.0001452867,0.002173602,0.0001961429,0.0002067415,0.001209283,0.006521081,0.00205593,0.00001414773,0.5097358,0.4767891,0.0004023349],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840052,0.002938999,0.001184443,0.03810073,0.04395991,0.0007936026,0.0004040737,0.0001036984,0.1285094],"genre_scores_gemma":[0.9790783,0.0000196744,0.001643066,0.0002821111,0.01774338,0.000002632308,0.0000114087,0.00005280791,0.001166629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.654525,"threshold_uncertainty_score":0.973653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105331891964563,"score_gpt":0.2486720895870627,"score_spread":0.2176187706674171,"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."}}