{"id":"W4387363317","doi":"10.2139/ssrn.4567815","title":"Wisdom of Crowds along the Supply Chain: Causal Evidence from Trade Credit","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Working Capital and Financial Performance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Simon Fraser University","funders":"","keywords":"Crowds; Supply chain; Business; Economics; Computer science; Computer security; Marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.001471773,0.0001577399,0.0001960539,0.0001412956,0.0003643224,0.0001492688,0.0005396472,0.0000646867,0.0000735539],"category_scores_gemma":[0.000127682,0.0001105244,0.0001320262,0.0007293267,0.0001052905,0.0008616659,0.000112365,0.001022358,0.0001735095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001076821,"about_ca_system_score_gemma":0.000316688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144287,"about_ca_topic_score_gemma":0.001656911,"domain_scores_codex":[0.9977593,0.0000140515,0.0003341534,0.000177462,0.0003837702,0.001331225],"domain_scores_gemma":[0.9993712,0.00009562697,0.000275508,0.0001903534,0.00005519877,0.00001206082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007139312,0.0002099976,0.4083183,0.0002255023,0.0005777876,0.0001054529,0.001803628,0.00115348,0.00681949,0.3648236,0.03866732,0.1765815],"study_design_scores_gemma":[0.001201875,0.0001788387,0.5528467,0.000703885,0.0002000879,0.00007134167,0.002458268,0.005180549,0.0003390096,0.4043139,0.03185808,0.0006475117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877836,0.003225751,0.00029952,0.007085869,0.0008886188,0.0001185317,0.000002762964,0.000073688,0.0005216858],"genre_scores_gemma":[0.9923965,0.002255189,0.000007401648,0.0002830833,0.004605767,0.000005777704,0.000009001756,0.0000239321,0.0004133282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.175934,"threshold_uncertainty_score":0.4507053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461574616698597,"score_gpt":0.2172868741045101,"score_spread":0.2026711279375241,"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."}}