{"id":"W109013405","doi":"10.1006/jaer.2000.0630","title":"PA—Precision Agriculture","year":2001,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering Research","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":689,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Precision agriculture; Weed; Remote sensing; Computer science; Field (mathematics); Scale (ratio); Agriculture; Agricultural engineering; Geography; Cartography; Engineering; Agronomy; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001088557,0.0002083901,0.0002679015,0.0001131766,0.0001612689,0.0001314214,0.0005896487,0.0001774029,0.000335751],"category_scores_gemma":[0.0005226343,0.0001026122,0.000188145,0.001497993,0.00007183461,0.0005046034,0.0002157225,0.001187684,0.0003416689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004690927,"about_ca_system_score_gemma":0.00001109686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004491921,"about_ca_topic_score_gemma":0.00001331484,"domain_scores_codex":[0.9969637,0.0001042559,0.0004568552,0.0002272138,0.001676611,0.0005713086],"domain_scores_gemma":[0.9988613,0.0002263767,0.0001496329,0.0001808341,0.0002685894,0.000313272],"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":[0.00003385194,0.0001156376,0.002522518,0.00001168184,0.00004157331,0.0002404795,0.0004042375,0.1116479,0.738349,0.00004642422,0.1401577,0.006429053],"study_design_scores_gemma":[0.0004818474,0.0003566325,0.8317403,0.0001951263,0.00001844425,0.004002074,0.0004584837,0.0003998374,0.01163027,0.00006935837,0.1502887,0.0003589169],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921976,0.0002432325,0.0001997644,0.00130284,0.0004300527,0.0001600909,7.562246e-7,0.00003954237,0.005426119],"genre_scores_gemma":[0.9911162,0.0002943418,0.004327685,0.00002246054,0.000810645,9.821442e-7,0.000002906805,0.00001521423,0.003409511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8292178,"threshold_uncertainty_score":0.5159963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176057944710286,"score_gpt":0.2677633606700754,"score_spread":0.2501575661990468,"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."}}