{"id":"W2924651880","doi":"10.1016/j.forpol.2019.03.004","title":"Technical change and productivity growth in the Alberta logging industry","year":2019,"lang":"en","type":"article","venue":"Forest Policy and Economics","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Logging; Productivity; Technical change; Incentive; Production (economics); Natural resource economics; Forest management; Total factor productivity; Forest industry; Technical progress; Business; Agricultural economics; Economics; Environmental resource management; Environmental science; Forestry; Agroforestry; Economic growth; Geography; Microeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004982125,0.0001660429,0.0003337517,0.0001941043,0.00007259116,0.0001186463,0.0002195434,0.0002499305,0.00002496246],"category_scores_gemma":[0.00008895211,0.0001664881,0.00004601121,0.0001112361,0.00009963918,0.0004994497,0.0001228962,0.0003510291,0.0001516758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000888446,"about_ca_system_score_gemma":0.00001813146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006955012,"about_ca_topic_score_gemma":0.00291537,"domain_scores_codex":[0.9987931,0.00001141644,0.0003763447,0.0004400416,0.000009248293,0.0003698666],"domain_scores_gemma":[0.9993908,0.00009322299,0.0001527658,0.0002829812,0.000003840275,0.00007634751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000006587261,0.00002247496,0.4908377,0.00002061508,0.000005246217,3.967398e-7,0.0002967734,0.00002135869,3.800955e-7,0.5085756,0.00005309939,0.0001597762],"study_design_scores_gemma":[0.0005838265,0.00007732476,0.7765684,0.00001312777,0.000003666619,0.00006109947,0.0001386753,0.001565132,0.000004821426,0.1764955,0.04416471,0.0003236676],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564998,0.0002948033,0.000003305628,0.007413771,0.000138271,0.0003445587,0.00005016257,0.00001116851,0.03524421],"genre_scores_gemma":[0.9969067,0.0006060185,0.00005999327,0.001760747,0.0003575409,0.00003203161,0.000008793742,0.00001720593,0.0002509688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3320801,"threshold_uncertainty_score":0.9996578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07182188579965727,"score_gpt":0.2279499264791513,"score_spread":0.156128040679494,"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."}}