{"id":"W4394287436","doi":"10.6084/m9.figshare.22274671.v1","title":"Clustered coefficient regression models for Poisson process with an application to seasonal warranty claim data","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Warranty; Poisson regression; Statistics; Econometrics; Regression; Poisson distribution; Regression analysis; Count data; Mathematics; Computer science; Medicine; Political science; Environmental health","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":[0.003457049,0.001332333,0.000969702,0.002291014,0.0005660024,0.001289991,0.00332882,0.001708904,0.00837402],"category_scores_gemma":[0.011159,0.0004021307,0.001440403,0.003420494,0.0003870352,0.001146592,0.001059342,0.002107745,0.006417416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001352763,"about_ca_system_score_gemma":0.001145425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01775129,"about_ca_topic_score_gemma":0.02659051,"domain_scores_codex":[0.9988025,0.0005226592,0.0001383535,0.0002447186,0.0002041254,0.00008763321],"domain_scores_gemma":[0.9964515,0.001868652,0.0003390066,0.0007137648,0.0005278426,0.00009913896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006554238,0.0008920762,0.02341693,0.001410875,0.0003111777,0.0005176981,0.0001808875,0.1377099,0.0008101613,0.01930264,0.7330162,0.08177616],"study_design_scores_gemma":[0.001146171,0.0002428212,0.02545335,0.0003258903,0.0001990158,0.0006329543,0.0003117417,0.6260867,0.002397073,0.04338121,0.2996124,0.0002106539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07610095,0.003477657,0.07297683,0.004129745,0.0004965015,0.0006785669,0.8302908,0.006093225,0.005755765],"genre_scores_gemma":[0.104291,0.001580085,0.07707854,0.0005079565,0.0001923764,0.001274651,0.8099454,0.0003930733,0.004736868],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01775129,"threshold_uncertainty_score":0.0352959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3173242172896739,"score_gpt":0.453790633650664,"score_spread":0.1364664163609901,"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."}}