{"id":"W2809444936","doi":"10.3390/su10082768","title":"Evaluating Greenhouse Tomato and Pepper Input Efficiency Use in Kosovo","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Manitoba Agriculture, Food and Rural Development","keywords":"Greenhouse; Pepper; Data envelopment analysis; Agricultural engineering; Agriculture; Agricultural science; Production (economics); Efficiency; Environmental science; Mathematics; Agricultural economics; Agronomy; Economics; Horticulture; Statistics; Engineering; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01545206,0.000231909,0.0004493506,0.0006464994,0.000405001,0.0005033683,0.0007432741,0.0001301737,0.0002170583],"category_scores_gemma":[0.08518574,0.0001794543,0.0001209215,0.003071532,0.001118241,0.0007612326,0.0005209952,0.0002414747,0.00009130473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004966836,"about_ca_system_score_gemma":0.0006016612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001010162,"about_ca_topic_score_gemma":0.001738521,"domain_scores_codex":[0.9941943,0.001129508,0.001062659,0.001237625,0.001715273,0.0006606606],"domain_scores_gemma":[0.992005,0.002852933,0.0002632581,0.001632877,0.003064886,0.0001810389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000835536,0.0003039272,0.9555128,0.00002536984,0.000006963963,0.00002442946,0.005203038,0.00126709,0.0002069765,0.00201379,0.00030043,0.03505163],"study_design_scores_gemma":[0.0006254725,0.0003726045,0.7984471,0.00002023207,0.00002319709,0.00001204803,0.003273744,0.1204893,0.0002739836,0.07507493,0.0009995548,0.0003878023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954252,0.00006941066,0.002070577,0.001344793,0.0001365175,0.0004413005,0.000003214201,0.00008059462,0.0004283562],"genre_scores_gemma":[0.9977301,8.946095e-7,0.001227203,0.00022713,0.00006411832,0.00001406199,6.310198e-7,0.00001425016,0.0007216558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1570657,"threshold_uncertainty_score":0.9225201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093101771542968,"score_gpt":0.4449918010758541,"score_spread":0.3356816239215573,"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."}}