{"id":"W2137022383","doi":"10.1136/oem.2010.055913","title":"Understanding changes over time in workers' compensation claim rates using time series analytical techniques","year":2011,"lang":"en","type":"article","venue":"Occupational and Environmental Medicine","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; Public Health Ontario; University of Toronto","funders":"Workplace Safety and Insurance Board","keywords":"Estimator; Compensation (psychology); Econometrics; Unemployment; Business cycle; Moving average; Series (stratigraphy); Unemployment rate; Statistics; Economics; Mathematics; Demographic economics; Macroeconomics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002469275,0.0001236945,0.0002005358,0.0001102921,0.0003038511,0.000001911068,0.00003504257,0.00007848386,0.003106668],"category_scores_gemma":[0.00001870595,0.00009514196,0.00001279757,0.00007483651,0.0002624065,0.0001219108,0.00006992663,0.0001326745,0.00003226679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002175307,"about_ca_system_score_gemma":0.00001126128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002033787,"about_ca_topic_score_gemma":0.00007138378,"domain_scores_codex":[0.999143,0.00008761969,0.0002316674,0.0001664798,0.0001798148,0.0001914],"domain_scores_gemma":[0.9996706,0.0001256804,0.00008136805,0.00006344083,0.000004964143,0.00005392106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001814605,0.00003363449,0.993669,0.0000209584,0.00002206009,0.000003494751,0.001922288,4.037809e-7,0.001494461,0.0008777318,0.00152412,0.0002504324],"study_design_scores_gemma":[0.0005797056,0.0001725469,0.9931228,0.0003041402,0.00003405647,0.000002101259,0.002472755,0.0007973122,0.00006685886,0.001612246,0.0007009102,0.0001345512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915597,0.0001330369,0.0001774456,0.001826981,0.00007717979,0.0003691542,0.00002277286,0.00004101641,0.005792742],"genre_scores_gemma":[0.9973084,0.0001238898,0.0002641246,0.0004434415,0.0001803153,0.00002302702,0.000130978,0.00001213311,0.001513721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005748699,"threshold_uncertainty_score":0.9978046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1906749579321309,"score_gpt":0.3849447884333602,"score_spread":0.1942698305012293,"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."}}