{"id":"W2137977379","doi":"10.5539/jas.v7n1p43","title":"Climate Change and Wheat Production in Drought Prone Areas of Bangladesh – A Technical Efficiency Analysis","year":2014,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Production (economics); Inefficiency; Irrigation; Agricultural science; Agriculture; Environmental science; Fertilizer; Agricultural machinery; Mathematics; Agricultural economics; Agricultural engineering; Agronomy; Geography; Economics; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001688319,0.0001395802,0.0003760048,0.00008367647,0.0001809919,0.0001035218,0.000386478,0.00006804388,0.00002345499],"category_scores_gemma":[0.0002504559,0.00004184895,0.0001278435,0.002381339,0.0002387932,0.001267848,0.0001075126,0.0001887464,0.000001707229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006021418,"about_ca_system_score_gemma":0.000008603594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001043324,"about_ca_topic_score_gemma":0.0006943325,"domain_scores_codex":[0.9983973,0.00007938986,0.000542537,0.0002925056,0.0003784259,0.0003098648],"domain_scores_gemma":[0.9986938,0.0001397945,0.0006874124,0.00004929192,0.0002867098,0.0001430321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007299368,0.0002979619,0.1104835,0.00002291552,0.00002956288,0.000002928294,0.00043526,0.000152301,0.8231246,0.000836157,0.00006636451,0.06447544],"study_design_scores_gemma":[0.00009832179,0.0003838412,0.9938305,0.00005352102,0.00007067647,0.0001033514,0.0003938681,0.00007419055,0.004574927,0.00006771356,0.000217053,0.0001320379],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952919,0.0002209214,0.000001192327,0.003709862,0.0001241871,0.000178407,0.000003649633,0.000008377452,0.0004615334],"genre_scores_gemma":[0.999073,0.000356566,0.0002246777,0.00003738875,0.0002842069,0.000004788662,0.000002924039,3.997391e-7,0.00001609638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.883347,"threshold_uncertainty_score":0.1706551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203339337535694,"score_gpt":0.2329199477044116,"score_spread":0.2125860139508422,"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."}}