{"id":"W6931090778","doi":"10.5281/zenodo.3246394","title":"An ERP study on the processing of agreement and tense violations in Arabic","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Diabetes Treatment and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agreement; Arabic; Measure (data warehouse); Semantics (computer science); Task (project management)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004820948,0.0003777011,0.0003529453,0.0002489604,0.000519143,0.0007540424,0.0004068164,0.0007372213,0.01060216],"category_scores_gemma":[0.004748538,0.0003845989,0.0002465051,0.0005420778,0.0008710004,0.001171283,0.0006918817,0.001183635,0.001040103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002434308,"about_ca_system_score_gemma":0.0003665687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003654557,"about_ca_topic_score_gemma":0.004225153,"domain_scores_codex":[0.9998434,0.00004307767,0.00001034905,0.00004197504,0.00003794948,0.00002330906],"domain_scores_gemma":[0.9984841,0.001098263,0.0001481555,0.00009486401,0.000108458,0.00006622391],"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":[0.006507493,0.000805154,0.01744085,0.001396169,0.0001398551,0.004128431,0.01496902,0.0003960294,0.8434837,0.00517441,0.003221017,0.1023378],"study_design_scores_gemma":[0.0009287511,0.002419179,0.9049474,0.0003439396,0.0004384513,0.01095248,0.009726933,0.002996153,0.04220207,0.01254663,0.01235807,0.0001399203],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756089,0.001201147,0.002690354,0.0003479647,0.00009892103,0.0002398502,0.000688573,0.00005054561,0.01907367],"genre_scores_gemma":[0.9920338,0.0006854608,0.00255103,0.0004900485,0.00005950507,0.0001630398,0.000440096,0.00007229853,0.003504703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01060216,"threshold_uncertainty_score":0.03546768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05373958165768941,"score_gpt":0.2883538844887115,"score_spread":0.2346143028310221,"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."}}