{"id":"W3111188917","doi":"10.3390/rs12244091","title":"Design and Development of a Smart Variable Rate Sprayer Using Deep Learning","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Dalhousie University; Agriculture and Agri-Food Canada; University of Prince Edward Island","funders":"","keywords":"Sprayer; Volume (thermodynamics); Variable (mathematics); Environmental science; Computer science; Mathematics; Artificial intelligence; Agronomy; Biology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005559593,0.000444858,0.0004074216,0.0002086134,0.000151913,0.0003308661,0.001216819,0.0006362158,0.003722724],"category_scores_gemma":[0.0003857586,0.0003690844,0.0003761338,0.0001041303,0.000260656,0.0004697176,0.0004195529,0.0006231145,0.0008613516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005289279,"about_ca_system_score_gemma":0.0009745519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001647386,"about_ca_topic_score_gemma":0.001684964,"domain_scores_codex":[0.9997897,0.00001921861,0.00001120033,0.0000725489,0.00007407193,0.00003334788],"domain_scores_gemma":[0.9998108,0.00003594303,0.00003129979,0.00001916753,0.00006985875,0.00003294586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008372309,0.000705953,0.004832973,0.0007012186,0.0001121709,0.0003157636,0.0001318095,0.3115344,0.4042298,0.004342197,0.005301459,0.266955],"study_design_scores_gemma":[0.00007507553,0.0007439835,0.001440995,0.00001456225,0.00002653377,0.00006615821,0.00001159974,0.9171259,0.0744157,0.0004865182,0.005570934,0.00002195871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07674748,0.0001544032,0.9105611,0.0002577268,0.00009286936,0.001043655,0.0003202696,0.006589515,0.004232927],"genre_scores_gemma":[0.5465671,0.0001599981,0.4406844,0.0003156204,0.00001744346,0.001348541,0.000294402,0.0002038141,0.01040856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003722724,"threshold_uncertainty_score":0.01245373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569289772247602,"score_gpt":0.2137097422580894,"score_spread":0.1680168445356134,"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."}}