{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001499348,0.0001101086,0.00008242304,0.00001649508,0.0001471199,0.00004513995,0.00005587634,0.00006350313,0.0004579652],"category_scores_gemma":[0.0000816147,0.00008072108,0.00003812118,0.0002242965,0.00007463939,0.0001483945,0.00003805229,0.00008577728,0.0002931216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001996648,"about_ca_system_score_gemma":0.000004338871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101811,"about_ca_topic_score_gemma":0.000240125,"domain_scores_codex":[0.9991092,0.0000715901,0.0001292575,0.0002389821,0.0002384971,0.0002124894],"domain_scores_gemma":[0.999682,0.00001737723,0.00004865922,0.0001809294,0.000008811438,0.00006218564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002900418,0.00001643119,0.0002519516,0.000004356958,0.000005887096,0.00002018456,0.0004012754,0.00678037,0.9028245,0.0002079064,0.002956987,0.08652726],"study_design_scores_gemma":[0.000860706,0.00006952277,0.03796818,0.0001017142,0.00009409804,0.0009189611,0.00116441,0.5681788,0.3006302,0.008744552,0.07987387,0.001394992],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6836945,0.000006179312,0.07463406,0.0001749994,0.0002311056,0.0001585811,1.987434e-7,0.00008244011,0.2410179],"genre_scores_gemma":[0.743548,0.000001041494,0.2512622,0.0002196934,0.00002681915,8.790407e-9,0.000001549029,0.00001180149,0.004928894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6021943,"threshold_uncertainty_score":0.5014399,"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."}}