{"id":"W4240504270","doi":"10.3386/w18724","title":"Measuring Margin","year":2013,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Margin (machine learning); Geology; Geography; Computer science; Machine learning","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":[0.001950797,0.0004512547,0.0003375901,0.003535114,0.000424653,0.001748409,0.0005889542,0.0006987606,0.01083972],"category_scores_gemma":[0.009884624,0.0001624968,0.000227188,0.002958779,0.0003697546,0.002492688,0.001424441,0.0005173289,0.006385482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005402312,"about_ca_system_score_gemma":0.0005344021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002338199,"about_ca_topic_score_gemma":0.002069563,"domain_scores_codex":[0.9971598,0.0004265767,0.0002448073,0.0004246967,0.001559587,0.0001845335],"domain_scores_gemma":[0.9933411,0.001163959,0.00180287,0.0007529972,0.002648545,0.0002904426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004449269,0.0002902398,0.3552232,0.0005782701,0.0001014338,0.0001538481,0.001811458,0.004992822,0.01861199,0.03867767,0.04987673,0.5292375],"study_design_scores_gemma":[0.00003981984,0.0008860545,0.5339432,0.0006876857,0.0001296352,0.001510635,0.005290215,0.01182034,0.07270425,0.04297522,0.3297772,0.0002358054],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4623938,0.007125956,0.1285016,0.001989282,0.0006964509,0.0007675869,0.02669529,0.001765379,0.3700647],"genre_scores_gemma":[0.9143177,0.002623066,0.0412993,0.0004679817,0.0001730651,0.0002824524,0.01000149,0.000193977,0.03064092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01083972,"threshold_uncertainty_score":0.03626251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6096209847576881,"score_gpt":0.4742770133657718,"score_spread":0.1353439713919163,"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."}}