{"id":"W4416975723","doi":"10.23865/magma.v28.1519","title":"Utfordringer i hvordan likviditet analyseres – og et forslag til løsning","year":2025,"lang":"sv","type":"article","venue":"Magma","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Market liquidity; Stylized fact; Database transaction; Cash; Capital (architecture); Working capital","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"],"consensus_categories":[],"category_scores_codex":[0.0006737372,0.000369094,0.0007570362,0.0008712022,0.0004209508,0.0003753954,0.0004465294,0.0003068375,0.0006738509],"category_scores_gemma":[0.0003920881,0.0004712136,0.0004234227,0.001174499,0.0001270686,0.0003944041,0.0002466133,0.000417609,0.0005254382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000205503,"about_ca_system_score_gemma":0.0001311639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007524781,"about_ca_topic_score_gemma":0.0007208845,"domain_scores_codex":[0.9974021,0.00003299812,0.001067535,0.0007685763,0.00008009272,0.0006486614],"domain_scores_gemma":[0.9984151,0.0001738072,0.0004202031,0.0007771319,0.00008290783,0.0001308454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004080567,0.0001900316,0.3583136,0.0001170734,0.0001692153,0.00001358247,0.0008491749,0.0008050666,0.00002304212,0.5669773,0.04710302,0.02539812],"study_design_scores_gemma":[0.0004967137,0.00005061508,0.3994289,0.0001043362,0.00004991552,0.000001340968,0.0001395803,0.004945726,0.00004855857,0.01902113,0.5753513,0.0003618705],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7745981,0.01810506,0.01773481,0.006269479,0.004368429,0.000476378,0.0004793187,0.0001256217,0.1778429],"genre_scores_gemma":[0.9429363,0.001703314,0.0009891224,0.0003638994,0.0004927214,0.0000380723,0.00008820913,0.00004831785,0.05334007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5479562,"threshold_uncertainty_score":0.999774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02430867546086554,"score_gpt":0.2686180537822557,"score_spread":0.2443093783213902,"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."}}