{"id":"W2166994821","doi":"","title":"Seasonal Work and Employment Insurance Use","year":2003,"lang":"en","type":"article","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Seasonality; Work (physics); Variety (cybernetics); Demographic economics; Incidence (geometry); Longitudinal data; Geography; Actuarial science; Business; Economics; Demography; Statistics; Engineering; Mathematics; Sociology","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005313795,0.0003392702,0.0005404583,0.0001787701,0.001736371,0.00001139409,0.0004233436,0.0001603519,0.0006195141],"category_scores_gemma":[0.0002938327,0.0003672147,0.0001983655,0.0002974606,0.0007319148,0.0002818048,0.0006027948,0.0007563914,0.00004788684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511675,"about_ca_system_score_gemma":0.0002297499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001544752,"about_ca_topic_score_gemma":0.002656113,"domain_scores_codex":[0.9969407,0.0009698942,0.0003574906,0.000556846,0.0004688045,0.0007062921],"domain_scores_gemma":[0.9976673,0.0009670246,0.0002778795,0.0005590399,0.0002163302,0.0003124414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005261702,0.0001961812,0.9463628,0.0001458923,0.0002647514,0.00006232787,0.02692008,0.000004934209,0.0001000578,0.01139904,0.01007737,0.003940418],"study_design_scores_gemma":[0.001846649,0.0001521504,0.6883616,0.0003561823,0.0000692942,0.000004459984,0.01234698,0.00003195552,0.000006997963,0.0007136948,0.2957454,0.0003646414],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676694,0.0007194034,0.000133175,0.001878422,0.0001903419,0.0006054518,0.000246647,0.0001042753,0.02845286],"genre_scores_gemma":[0.9713339,0.001767402,0.002587343,0.0003856022,0.00004434125,0.000003856687,0.00004675879,0.00004085769,0.02378993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.285668,"threshold_uncertainty_score":0.999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765424896112729,"score_gpt":0.2926715868091967,"score_spread":0.2450173378480694,"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."}}