{"id":"W4405171652","doi":"10.1139/cjfr-2024-0213","title":"The impact of imported timber price fluctuations on the margins of wood forest product exports: empirical evidence from provincial-level data in China","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Margin (machine learning); Forest product; China; Business; Product (mathematics); Investment (military); Agricultural economics; Supply chain; Economics; Natural resource economics; Forest management; Forestry; Geography","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":[0.001060438,0.0002658731,0.0003595703,0.00118243,0.000600896,0.0009208609,0.0005096974,0.0001983977,0.001307021],"category_scores_gemma":[0.003547664,0.0002059239,0.0007068891,0.002744153,0.0005473692,0.0006129564,0.0008038948,0.0004263178,0.0001895615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829463,"about_ca_system_score_gemma":0.002173224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4178814,"about_ca_topic_score_gemma":0.4717637,"domain_scores_codex":[0.9993998,0.0000823837,0.00005540652,0.0001380062,0.0001665529,0.0001578752],"domain_scores_gemma":[0.9960414,0.001007298,0.001328785,0.0004507762,0.0007949987,0.0003767436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004567809,0.00001520569,0.9940355,0.00001466159,0.00008730758,0.0001075994,0.0001877503,0.00214308,0.0001700705,0.0002418815,0.0003236785,0.002627572],"study_design_scores_gemma":[0.000004191114,0.00001121103,0.9941385,0.00000600315,0.0000401474,0.00002127737,0.0002555722,0.004818316,0.0001531073,0.00009228219,0.0004514738,0.000007801322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980446,0.0000935519,0.0001563889,0.00004445433,0.000001863492,0.000002456108,0.001167744,0.000008332466,0.0004805427],"genre_scores_gemma":[0.9975271,0.00007095624,0.00006732452,0.000008317152,0.000002633583,0.000001967211,0.002112682,0.000002589459,0.0002064922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4178814,"threshold_uncertainty_score":0.8308982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3325211541360765,"score_gpt":0.3671272821682871,"score_spread":0.03460612803221053,"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."}}