{"id":"W4252967254","doi":"10.1016/s0169-5150(01)00078-0","title":"Alternative methods for environmentally adjusted productivity analysis","year":2001,"lang":"en","type":"article","venue":"Agricultural Economics","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Productivity; Productivity model; Nonparametric statistics; Agricultural productivity; Production (economics); Econometrics; Strengths and weaknesses; Economics; Index (typography); Environmental economics; Agriculture; Computer science; Microeconomics; Total factor productivity; Macroeconomics; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004213544,0.000225342,0.0002979307,0.00003121467,0.0001759778,0.00004727173,0.0002821978,0.0000728805,0.001256646],"category_scores_gemma":[0.00004960346,0.0001690178,0.0002766162,0.0002460604,0.0001826237,0.0005511895,0.0002230059,0.00009394708,0.000107017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042306,"about_ca_system_score_gemma":0.000004117836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004830809,"about_ca_topic_score_gemma":0.0003746641,"domain_scores_codex":[0.9986342,0.00007860124,0.0002756966,0.0005613645,0.00007011279,0.0003800732],"domain_scores_gemma":[0.9992959,0.00009579022,0.0001481864,0.0003005968,0.000004335325,0.000155172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002002651,0.0007880164,0.7400716,0.00001668215,0.001077985,0.000003761744,0.001344224,0.1179657,0.03278923,0.0006142132,0.001002408,0.1041259],"study_design_scores_gemma":[0.0003912943,0.0001176312,0.9646167,8.172086e-7,0.0002978889,0.00001138544,0.000597267,0.006073212,0.008939272,0.00186055,0.01669621,0.0003977456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887072,0.00002277082,0.006257849,0.0005187225,0.00009097793,0.000628873,0.00002067617,0.00003187322,0.003721],"genre_scores_gemma":[0.9784723,0.00007357586,0.01803059,0.0001528913,0.0000683828,0.0000943776,0.00008704683,0.00001186809,0.003008966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2245451,"threshold_uncertainty_score":0.9996563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01591103475152748,"score_gpt":0.2773551700374889,"score_spread":0.2614441352859614,"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."}}