{"id":"W4382073822","doi":"10.22616/erdev.2023.22.tf203","title":"Assessment of energy and environmental impact in precision seeding technological processes","year":2023,"lang":"en","type":"article","venue":"Engineering for Rural Development","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Lietuvos Mokslo Taryba","keywords":"Seeding; Precision agriculture; Environmental science; Yield (engineering); Variable (mathematics); Agricultural engineering; Agronomy; Materials science; Mathematics; Engineering; Agriculture; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006784533,0.0004519366,0.0004245249,0.0004771661,0.0002942087,0.000752299,0.0003234781,0.000480882,0.0006776575],"category_scores_gemma":[0.0007325721,0.000178249,0.0004937389,0.0009469083,0.0003036767,0.0004939702,0.0004082599,0.0003543355,0.0001252861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006294888,"about_ca_system_score_gemma":0.0002681999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002460144,"about_ca_topic_score_gemma":0.003530172,"domain_scores_codex":[0.9993441,0.00007891405,0.00003988299,0.0001085983,0.0003663756,0.00006221071],"domain_scores_gemma":[0.9993857,0.0002173125,0.0001191786,0.00006025425,0.000196918,0.00002060608],"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":[0.001242641,0.0002332278,0.04549903,0.0004831185,0.00008407547,0.0004443963,0.0001617221,0.02239493,0.8894016,0.0002672265,0.00009792975,0.03969022],"study_design_scores_gemma":[0.00005237311,0.004509229,0.239303,0.0000392664,0.0002376724,0.0002628719,0.0004996619,0.02467286,0.7268916,0.000401784,0.00307098,0.00005868341],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957843,0.0003447867,0.002702183,0.000009934322,0.000005620691,0.00004030163,0.000118139,0.00001605227,0.0009785595],"genre_scores_gemma":[0.9958748,0.0003893877,0.00302506,0.000008921068,0.000002579427,0.00002620273,0.0001608241,0.00001034059,0.0005017836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002460144,"threshold_uncertainty_score":0.004891634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005103072956258051,"score_gpt":0.2233875880829125,"score_spread":0.2182845151266544,"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."}}