{"id":"W1522644772","doi":"10.1007/978-3-642-14049-5_36","title":"Data Mining in Precision Agriculture: Management of Spatial Information","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Field (mathematics); Data mining; Machine learning; Spatial analysis; Task (project management); Precision agriculture; Global Positioning System; Variety (cybernetics); Regression; Artificial neural network; Support vector machine; Big data; Artificial intelligence; Independence (probability theory); Agriculture; Statistics; Geography; Mathematics","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":[0.002386761,0.001137373,0.001806827,0.002456424,0.0005756848,0.004427411,0.003190009,0.001532563,0.004667543],"category_scores_gemma":[0.004424041,0.001081131,0.001232679,0.008743813,0.001661789,0.005814127,0.002167603,0.002732219,0.003028773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000931944,"about_ca_system_score_gemma":0.001517499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002210312,"about_ca_topic_score_gemma":0.002737584,"domain_scores_codex":[0.9984933,0.0003190994,0.0001538767,0.0003431383,0.0006374819,0.00005296684],"domain_scores_gemma":[0.9971379,0.001745563,0.0001217095,0.0005377306,0.0003765624,0.00008048803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003493248,0.00007250877,0.0008743429,0.001654547,0.0001101686,0.0001336898,0.0003167692,0.01111628,0.001961442,0.09820233,0.07840596,0.807117],"study_design_scores_gemma":[0.00002486776,0.00008274237,0.002087694,0.0009302288,0.0001045038,0.001099701,0.0002854446,0.1104387,0.007470327,0.4252527,0.4521267,0.00009645376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002559179,0.05822007,0.9195225,0.004860983,0.001015551,0.0001085459,0.001138745,0.001630572,0.01094379],"genre_scores_gemma":[0.03071638,0.0608134,0.8865843,0.0012096,0.001225799,0.0001851174,0.003132541,0.0005529986,0.01557983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004667543,"threshold_uncertainty_score":0.01561451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491412883622832,"score_gpt":0.2296505248709205,"score_spread":0.2147363960346922,"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."}}