{"id":"W2051179653","doi":"10.4067/s0718-221x2007000100004","title":"Exportación de madera aserrada de conífera chilena. Un análisis de su competitividad","year":2009,"lang":"es","type":"article","venue":"Maderas Ciencia y tecnología","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Latin Americans; Geography; Art","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":[0.0006732788,0.0005147679,0.0003841694,0.00230242,0.0008144198,0.001529161,0.000263746,0.0002452572,0.003627647],"category_scores_gemma":[0.0004092094,0.0002128177,0.0005883407,0.003316256,0.0003084474,0.0009713438,0.000619088,0.000411562,0.0005235361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002046028,"about_ca_system_score_gemma":0.001031859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0529759,"about_ca_topic_score_gemma":0.09098484,"domain_scores_codex":[0.9997297,0.00002372872,0.00001472107,0.00009497583,0.00008659466,0.00005020287],"domain_scores_gemma":[0.9993382,0.0001031747,0.0002090566,0.00004485683,0.0002449807,0.00005967559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009597121,0.0001696737,0.8212323,0.001429419,0.0004107044,0.001131577,0.007575678,0.001648899,0.08438284,0.001145359,0.001501156,0.07841272],"study_design_scores_gemma":[0.000006806083,0.0001888089,0.9786476,0.00009733549,0.00006767698,0.0001941488,0.003690901,0.0002655778,0.003656768,0.0001086266,0.0130557,0.00002002496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880788,0.001193821,0.0002217489,0.00004886952,0.00000733506,0.00002448158,0.001978999,0.00001485108,0.008431134],"genre_scores_gemma":[0.9719301,0.002060661,0.0006292649,0.0000372156,0.00001186826,0.00007361897,0.003447622,0.00002267251,0.02178705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0529759,"threshold_uncertainty_score":0.1053351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154499991731916,"score_gpt":0.2248965307300203,"score_spread":0.2133515308127011,"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."}}