{"id":"W4406238578","doi":"10.1139/cjfr-2024-0177","title":"Impacts of COVID-19 and contractual changes on the financial performance of lumber futures","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Agriculture","keywords":"Futures contract; Autoregressive conditional heteroskedasticity; Futures market; Volatility (finance); Economics; Financial economics; Event study; Heteroscedasticity; Econometrics; Coronavirus disease 2019 (COVID-19); Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004025863,0.0002288139,0.0002255498,0.0005429967,0.0004768671,0.001743307,0.0003674422,0.0006313114,0.001364828],"category_scores_gemma":[0.0104017,0.00009114385,0.0003229807,0.0004901722,0.0007122244,0.001025278,0.0007241875,0.0008470224,0.0001294375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604513,"about_ca_system_score_gemma":0.001036055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009844659,"about_ca_topic_score_gemma":0.01040614,"domain_scores_codex":[0.9979887,0.0004857834,0.0001438091,0.0001917385,0.0008459631,0.0003439905],"domain_scores_gemma":[0.992651,0.002642735,0.002852927,0.0002885806,0.0009253771,0.0006393313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002130443,0.001271702,0.8881663,0.0001091188,0.0001860584,0.001851465,0.001003674,0.01990913,0.01979988,0.004936031,0.001334128,0.05930207],"study_design_scores_gemma":[0.00003671313,0.001570997,0.9649075,0.00002473002,0.00006278568,0.0003138231,0.001511613,0.01995106,0.00650557,0.001462702,0.003584631,0.0000678967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975485,0.0001278468,0.0001717783,0.0001048398,0.00001313688,0.00001249512,0.00007368223,0.00000571879,0.001942075],"genre_scores_gemma":[0.9994764,0.00005145996,0.0001140694,0.00003873411,0.000009499185,0.000004821758,0.00008788473,0.00000225098,0.0002149677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009844659,"threshold_uncertainty_score":0.02129102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289246259301453,"score_gpt":0.3197686757707663,"score_spread":0.2868762131777517,"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."}}