{"id":"W6944056072","doi":"10.17632/vnk92w3bpd.2","title":"Asset Life, Leverage, and Debt Maturity Matching","year":2024,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Replication (statistics); Maturity (psychological); Debt; Asset (computer security); Matching (statistics)","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":[0.001640836,0.001411655,0.001373666,0.003088737,0.0007372132,0.002258756,0.002936314,0.001613225,0.08607945],"category_scores_gemma":[0.01053374,0.0007273909,0.001276102,0.005247211,0.0003283619,0.0013796,0.00174315,0.001799593,0.1229016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476542,"about_ca_system_score_gemma":0.00204229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0252563,"about_ca_topic_score_gemma":0.04349764,"domain_scores_codex":[0.9985691,0.0002184626,0.0001988017,0.0004069363,0.000350465,0.0002562458],"domain_scores_gemma":[0.9955291,0.0008095242,0.0005481206,0.001340082,0.00147123,0.0003019411],"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":[0.00005246798,0.00002283337,0.001838219,0.0001752836,0.00001894857,0.00001206995,0.00001439425,0.0001488804,0.0000455718,0.0004410234,0.995487,0.001743284],"study_design_scores_gemma":[0.0003315609,0.00002533615,0.01554379,0.0003304984,0.00004364225,0.00008385895,0.0001348279,0.0006032155,0.0006099144,0.002627572,0.9796069,0.00005880183],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002140522,0.00001811549,0.00009423973,0.00004728897,0.00001849787,0.00001255474,0.9986933,0.0002004314,0.0007015679],"genre_scores_gemma":[0.0005857364,0.00002371783,0.0003696704,0.00004205644,0.000009175486,0.0001009288,0.9976121,0.00009710511,0.001159559],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08607945,"threshold_uncertainty_score":0.2879645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02605446715619903,"score_gpt":0.2421587810735533,"score_spread":0.2161043139173543,"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."}}