{"id":"W3145795761","doi":"","title":"An application of data envelopment analysis to investigate the efficiency of lumber industry in northwestern Ontario, Canada","year":2012,"lang":"en","type":"article","venue":"林业研究：英文版","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Operations research; Operations management; Business; Economics; Engineering; Statistics; Mathematics","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":[0.001566011,0.0004350508,0.000557951,0.003124189,0.002396348,0.002591311,0.0006524573,0.0004013325,0.001383984],"category_scores_gemma":[0.005068152,0.000283096,0.0007903705,0.008076147,0.0008594021,0.0007749434,0.0006527742,0.0004061497,0.0001087464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06234943,"about_ca_system_score_gemma":0.06358153,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946098,"about_ca_topic_score_gemma":0.9941418,"domain_scores_codex":[0.9985669,0.0002026825,0.00007435345,0.0001529079,0.0006628316,0.0003402733],"domain_scores_gemma":[0.9971082,0.0007054205,0.0002250024,0.00006468011,0.001714179,0.0001825999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003926626,0.000206862,0.846509,0.0002633086,0.0004468599,0.0006582493,0.005337734,0.08611911,0.001185509,0.009333165,0.004015475,0.04553209],"study_design_scores_gemma":[0.00003239507,0.00007638642,0.8891565,0.0001033021,0.000113923,0.00004855739,0.01740169,0.08394986,0.0008588425,0.001344549,0.00685496,0.00005902071],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838874,0.0003922154,0.002821062,0.0003815749,0.000007685167,0.0001034791,0.002074601,0.00002991937,0.01030204],"genre_scores_gemma":[0.9938617,0.000252787,0.002089615,0.00002074205,0.000002147187,0.00002723549,0.0009158575,0.000007077299,0.002823023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06234943,"threshold_uncertainty_score":0.4523789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996787126480955,"score_gpt":0.2502658749611452,"score_spread":0.2302980036963357,"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."}}