{"id":"W2290961178","doi":"","title":"Business environment description for softwood lumbers production in Brazil and Canada.","year":2011,"lang":"en","type":"article","venue":"FLORESTA","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Production (economics); Softwood; Business; Forensic engineering; Pulp and paper industry; Waste management; Engineering; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004647682,0.00006463355,0.00005192881,0.0000466594,0.00002083641,0.000008038764,0.00003224902,0.00002176158,0.00002354933],"category_scores_gemma":[0.00001156757,0.00006942776,0.000005520134,0.00006101175,0.00001454968,0.000103493,0.00001231728,0.00001988324,0.000001938233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009281023,"about_ca_system_score_gemma":0.00001189937,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02527814,"about_ca_topic_score_gemma":0.4858195,"domain_scores_codex":[0.999625,0.000004134333,0.00009146408,0.0001087239,0.00005981588,0.0001108933],"domain_scores_gemma":[0.9998632,0.000002298097,0.0000105312,0.00008727192,0.000009066951,0.00002759954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003185421,0.0004964009,0.5255798,0.003301428,0.0002588326,0.00005658802,0.006083266,0.084771,0.008937201,0.01608126,0.2705652,0.08355054],"study_design_scores_gemma":[0.0005931523,0.00002203127,0.8988433,0.00003555378,0.00001916039,0.000003048839,0.0002087453,0.009696644,0.001938877,0.0003357262,0.08803489,0.0002688873],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831374,0.0001184503,0.01336524,0.0001432971,0.0009263832,0.0005196423,0.00000808131,0.0001009108,0.001680621],"genre_scores_gemma":[0.9985134,0.0001027785,0.0009393392,0.00002614336,0.00003102255,0.00005288842,0.00002455686,0.00001432668,0.0002955539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4605414,"threshold_uncertainty_score":0.9812126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425389124976913,"score_gpt":0.1658338319681487,"score_spread":0.1515799407183796,"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."}}