{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003015845,0.0003294192,0.0004398454,0.001249526,0.0002916394,0.00109253,0.0002670361,0.0002160032,0.0005669422],"category_scores_gemma":[0.004058753,0.0001621681,0.0005431797,0.00201744,0.0004061664,0.0007581424,0.0006142377,0.0001794292,0.00005477131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002412936,"about_ca_system_score_gemma":0.001422674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0281408,"about_ca_topic_score_gemma":0.03886881,"domain_scores_codex":[0.9986808,0.0006549947,0.00009789348,0.0001330906,0.0002040219,0.0002291671],"domain_scores_gemma":[0.9978431,0.001335805,0.0004498122,0.00008001913,0.0002244492,0.00006679883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003334137,0.0002504962,0.7631898,0.0003001317,0.0003954381,0.0005238904,0.001427691,0.1916097,0.007005045,0.00423603,0.00022343,0.03050499],"study_design_scores_gemma":[0.00001073937,0.0003064073,0.9133741,0.00007420559,0.000100849,0.00006571443,0.003922149,0.07649296,0.003529567,0.0008798046,0.001212427,0.00003110077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983829,0.0000533643,0.0007904534,0.00001603914,5.53636e-7,0.00001069797,0.0001239622,0.000002805928,0.0006191649],"genre_scores_gemma":[0.9988779,0.00004870756,0.000786546,0.00000314412,4.92739e-7,0.00001014727,0.0001925734,0.000001908137,0.00007852328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0281408,"threshold_uncertainty_score":0.05595404,"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."}}