{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02151754,0.001788208,0.001644023,0.003952859,0.002693908,0.01321818,0.002995941,0.002785511,0.08442233],"category_scores_gemma":[0.0567218,0.001081695,0.003345321,0.003653049,0.003517711,0.01012134,0.006609364,0.005757691,0.02986343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005135999,"about_ca_system_score_gemma":0.009772282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021443,"about_ca_topic_score_gemma":0.01547206,"domain_scores_codex":[0.9783062,0.008736906,0.00172634,0.003279776,0.006908029,0.001042641],"domain_scores_gemma":[0.954944,0.02307154,0.003713361,0.006156855,0.01009435,0.002019794],"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":[0.001324987,0.0006880001,0.023431,0.01389426,0.0008038816,0.0008793879,0.02581424,0.001117354,0.005413243,0.04670237,0.202553,0.6773783],"study_design_scores_gemma":[0.00008145383,0.0003867747,0.01902866,0.007350411,0.0004143673,0.0005524848,0.01343013,0.0004487549,0.003447413,0.02403697,0.9306536,0.0001689579],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1101968,0.1527454,0.0884328,0.08877227,0.01807401,0.003142816,0.02086104,0.003734865,0.5140402],"genre_scores_gemma":[0.3554862,0.1139812,0.1130879,0.03044676,0.006498237,0.004746487,0.02353868,0.005188372,0.3470261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08442233,"threshold_uncertainty_score":0.2824208,"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."}}