{"id":"W2016240639","doi":"10.1080/01690960500372725","title":"Shallow semantic processing of text: Evidence from eye movements","year":2006,"lang":"en","type":"article","venue":"Language and Cognitive Processes","topic":"Reading and Literacy Development","field":"Psychology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Eye movement; Noun phrase; Phrase; Computer science; Semantic memory; Anomaly detection; Anomaly (physics); Comprehension; Natural language processing; Reading (process); Fixation (population genetics); Noun; Artificial intelligence; Semantics (computer science); Cognitive psychology; Psychology; Linguistics; Medicine; Cognition; Neuroscience","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":[0.0004656647,0.0002457858,0.0002018421,0.0006980235,0.0001742846,0.0005706479,0.0002637199,0.0006553037,0.001672843],"category_scores_gemma":[0.008834261,0.0003065627,0.0001360177,0.000449924,0.0007121137,0.001378124,0.0006839315,0.0005302156,0.0004037301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691273,"about_ca_system_score_gemma":0.0001775199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001303266,"about_ca_topic_score_gemma":0.001583871,"domain_scores_codex":[0.9995409,0.00009323783,0.00003622217,0.0001206814,0.0001574389,0.00005150443],"domain_scores_gemma":[0.9932969,0.003549726,0.001742982,0.0007701467,0.0005080102,0.0001321946],"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":[0.0007521727,0.0001463767,0.1089377,0.0004032096,0.00006270871,0.001024462,0.007180216,0.0003057378,0.7961545,0.001720943,0.0007859215,0.082526],"study_design_scores_gemma":[0.00003371197,0.0003040401,0.9396234,0.0000376028,0.0000332551,0.001399878,0.0009592202,0.001503148,0.05135426,0.002824779,0.001889302,0.00003731871],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931383,0.0001913044,0.002750795,0.0001099725,0.000006008279,0.00002082147,0.0001873463,0.00007246557,0.003522911],"genre_scores_gemma":[0.9967322,0.0001632606,0.002263351,0.0000614036,0.000006758259,0.00003617844,0.0001740264,0.00003042471,0.0005324001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001672843,"threshold_uncertainty_score":0.005596161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514639283550196,"score_gpt":0.3134153342098324,"score_spread":0.2982689413743304,"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."}}