{"id":"W4387738273","doi":"10.1108/jeas-05-2023-0108","title":"Impact of technical efficiency and input-driven growth in the Indian food processing sector","year":2023,"lang":"en","type":"article","venue":"Journal of economic and administrative sciences.","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Food processing; Productivity; Econometrics; Returns to scale; Technical change; Panel data; Economics; Resource efficiency; Originality; Industrial organization; Production (economics); Macroeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007006974,0.0001260169,0.0004012654,0.0008274472,0.0002270355,0.0003312475,0.0008425479,0.00005523055,0.00002727758],"category_scores_gemma":[0.00136485,0.00006659529,0.0001408567,0.001711708,0.001284789,0.0005668304,0.00008291478,0.000221834,0.000004491979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005939795,"about_ca_system_score_gemma":0.000955098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001627108,"about_ca_topic_score_gemma":0.0001072849,"domain_scores_codex":[0.9977324,0.000202118,0.0009600138,0.0003080301,0.0005583691,0.0002390748],"domain_scores_gemma":[0.9974802,0.0012228,0.0009237926,0.0001292582,0.0001389511,0.0001050739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008693169,0.0003306809,0.9600904,0.00002470633,0.00005425286,0.00008639789,0.01633766,0.004895049,0.002840561,0.007561016,0.0007712049,0.00692117],"study_design_scores_gemma":[0.000663755,0.003749659,0.947467,0.0001078962,0.00002720184,0.0004242197,0.01088601,0.01114221,0.0002520302,0.02500667,0.00004061556,0.0002326911],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974579,0.0001562125,0.0002373649,0.0009584069,0.00005662364,0.00008277599,0.00001016145,0.000003630285,0.001036928],"genre_scores_gemma":[0.9996632,0.00003347104,0.0002063714,0.0000358264,0.0000439355,9.763608e-7,2.387369e-7,0.000002933777,0.00001302939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01744566,"threshold_uncertainty_score":0.4733858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1367060805025915,"score_gpt":0.4240894113371685,"score_spread":0.287383330834577,"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."}}