{"id":"W2170431873","doi":"10.1109/igarss.2002.1026413","title":"Crop/weed discrimination using remote sensing","year":2003,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Weed; Spectroradiometer; Crop; Remote sensing; Precision agriculture; Environmental science; Agronomy; Reflectivity; Agriculture; Biology; Physics; Geography; Optics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004663901,0.000342631,0.0002625253,0.000850295,0.0001644278,0.0003536573,0.0002562047,0.000243623,0.001138124],"category_scores_gemma":[0.0004023184,0.0001566385,0.0001945813,0.0004299007,0.0002072192,0.0003685415,0.0002548861,0.0002057169,0.0003533587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001889808,"about_ca_system_score_gemma":0.0001995567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006090688,"about_ca_topic_score_gemma":0.01454809,"domain_scores_codex":[0.9998145,0.0000393204,0.000005392413,0.00005653019,0.0000602312,0.00002413375],"domain_scores_gemma":[0.9998672,0.00004330219,0.0000291415,0.0000133123,0.00003580619,0.0000112996],"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":[0.0003404383,0.00005395492,0.01499561,0.0001570987,0.00003231211,0.00010767,0.0001006231,0.002472723,0.8827583,0.0002821955,0.0002985314,0.09840056],"study_design_scores_gemma":[0.0001160868,0.0007222401,0.4045599,0.00006493407,0.0002519587,0.001503214,0.0005508614,0.1165891,0.4649889,0.002238231,0.008264789,0.0001499195],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9287948,0.0009108796,0.06271262,0.0001117815,0.00001754779,0.00008298425,0.0003595845,0.0006502903,0.006359577],"genre_scores_gemma":[0.9269738,0.0004515859,0.07075936,0.00006860021,0.00001040626,0.00002316212,0.0003639274,0.00002857299,0.001320526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006090688,"threshold_uncertainty_score":0.01211047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826711677098688,"score_gpt":0.2359611532046315,"score_spread":0.2176940364336446,"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."}}