{"id":"W4213350611","doi":"10.1002/9780470057339.vap034","title":"Precision Agriculture","year":2006,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Precision agriculture; Matching (statistics); Agriculture; Productivity; Field (mathematics); Agricultural engineering; Computer science; Crop productivity; Agricultural management; Geography; Engineering; Mathematics; Economics; Statistics; Archaeology","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":[0.000756271,0.001012466,0.0004892878,0.002221403,0.001105725,0.003400707,0.001256041,0.001123409,0.2515601],"category_scores_gemma":[0.001408257,0.0003833399,0.0004703845,0.002541091,0.0003736145,0.001411217,0.001740524,0.001206003,0.2046605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279426,"about_ca_system_score_gemma":0.001452572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004718933,"about_ca_topic_score_gemma":0.005944906,"domain_scores_codex":[0.9988901,0.00005462691,0.00003363695,0.0001979984,0.0007459841,0.00007772318],"domain_scores_gemma":[0.9985183,0.00009743628,0.00006394795,0.0003368713,0.0008695233,0.0001140011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008804249,0.00004434825,0.0007841187,0.0002238603,0.00001379755,0.000132342,0.00004616289,0.001201944,0.004995773,0.01445666,0.3807517,0.5972613],"study_design_scores_gemma":[0.00001116949,0.00002209786,0.0007161011,0.00004935152,0.000005513134,0.000141515,0.00001751455,0.0006522072,0.001999042,0.002561266,0.9938167,0.000007550854],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002041515,0.002901531,0.03926056,0.001660795,0.001073097,0.0002248175,0.01002289,0.009485555,0.9333292],"genre_scores_gemma":[0.02642518,0.003526677,0.02782908,0.0007496394,0.000445065,0.0001516998,0.01825902,0.001005827,0.9216077],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2515601,"threshold_uncertainty_score":0.8415523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005566200199790093,"score_gpt":0.1769832958491183,"score_spread":0.1714170956493282,"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."}}