{"id":"W2317282816","doi":"10.13073/0015-7473-60.7.709","title":"Estimating Regional Softwood Lumber Supply in the United States Using Seemingly Unrelated Regression","year":2010,"lang":"en","type":"article","venue":"Forest Products Journal","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stumpage; Softwood; Economics; Listing (finance); Agricultural economics; Engineering; Pulp and paper industry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000893593,0.0002585013,0.0002950629,0.001171485,0.0002095154,0.0005172345,0.0002983397,0.0001887971,0.001520495],"category_scores_gemma":[0.002819723,0.0002395518,0.0004960702,0.001383343,0.0001418117,0.0004624636,0.0003983074,0.0003054476,0.0003769824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006476945,"about_ca_system_score_gemma":0.0004334123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07425512,"about_ca_topic_score_gemma":0.09153141,"domain_scores_codex":[0.9996485,0.0001166611,0.00002641443,0.0001296473,0.00005151179,0.00002731349],"domain_scores_gemma":[0.9983072,0.0007973937,0.0004676644,0.0001218922,0.0002534305,0.00005224231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008336472,0.00006039874,0.9039824,0.00003184055,0.0002731449,0.0001918615,0.0001761443,0.07287621,0.0006363941,0.001303237,0.001018391,0.01936659],"study_design_scores_gemma":[0.00001806773,0.0001170535,0.7962499,0.00003015607,0.0001278234,0.0001643246,0.0005240417,0.1961386,0.001006774,0.00163682,0.003957365,0.00002916205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891131,0.0002045233,0.007731617,0.00004217467,0.000004299215,0.0000102194,0.00160574,0.00004830552,0.001239898],"genre_scores_gemma":[0.9898062,0.0001264258,0.004785032,0.00001894256,0.000006094218,0.00001641679,0.00440047,0.00001258802,0.0008276834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07425512,"threshold_uncertainty_score":0.1476458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02012801265640132,"score_gpt":0.2685958336766773,"score_spread":0.248467821020276,"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."}}