{"id":"W1485536751","doi":"10.1111/j.1539-6975.2013.12012.x","title":"<scp>S</scp><scp>OLVENCY</scp> A<scp>NALYSIS AND</scp> P<scp>REDICTION IN</scp> P<scp>ROPERTY</scp>–<scp>C</scp><scp>ASUALTY</scp> I<scp>NSURANCE</scp>: I<scp>NCORPORATING</scp> E<scp>CONOMIC AND</scp> M<scp>ARKET</scp> P<scp>REDICTORS</scp>","year":2013,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Insolvency; Solvency; Leverage (statistics); Business; Cash flow; Monetary economics; Economics; Actuarial science; Market liquidity; Finance; Computer science","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":["metaresearch","metaepi_narrow","metaepi_broad","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","metaepi_broad","sts","scholarly_communication","research_integrity"],"category_scores_codex":[0.02361096,0.015343,0.02147447,0.01554171,0.008870472,0.009589294,0.01548309,0.01046329,0.0001389031],"category_scores_gemma":[0.1158146,0.01670959,0.01015109,0.01892152,0.006460515,0.01930501,0.005932282,0.02032223,0.007675434],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007030127,"about_ca_system_score_gemma":0.004249442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005822236,"about_ca_topic_score_gemma":0.002698064,"domain_scores_codex":[0.9251146,0.004849717,0.02569649,0.01578024,0.008678709,0.01988023],"domain_scores_gemma":[0.8820573,0.05600755,0.03344912,0.01140388,0.007343449,0.009738669],"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":[0.0000861851,0.007373323,0.4313015,0.003625873,0.008829209,0.00249847,0.03681459,0.01354713,0.003554237,0.0053525,0.4798973,0.007119689],"study_design_scores_gemma":[0.01910203,0.004212829,0.2755478,0.003551825,0.003273213,0.001592617,0.04487444,0.01083974,0.004865356,0.0101044,0.6209477,0.001088046],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8188477,0.07618082,0.00479103,0.0003990369,0.01848627,0.01019257,0.008618901,0.00277222,0.05971147],"genre_scores_gemma":[0.7782136,0.1368477,0.005407011,0.004141049,0.01445396,0.003431514,0.002293108,0.004327576,0.05088443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1557537,"threshold_uncertainty_score":0.999762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447419822692725,"score_gpt":0.2073727521507677,"score_spread":0.1928985539238404,"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."}}