{"id":"W2101144152","doi":"10.82308/3826","title":"Artificial intelligence analysis of hyperspectral remote sensing data for management of water, weed, and nitrogen stresses in corn fields","year":2005,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Weed; Weed control; Irrigation; Randomized block design; Spectroradiometer; Agronomy; Growing season; Hyperspectral imaging; Environmental science; Mathematics; Precision agriculture; Geography; Remote sensing; Agriculture; Biology; Physics; Reflectivity","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":[0.0005799535,0.0002894215,0.0002302295,0.0004769972,0.0001347402,0.00036009,0.0001858454,0.0001772254,0.0001937227],"category_scores_gemma":[0.000960485,0.00007952909,0.0001959826,0.0002612169,0.0001350984,0.0002110669,0.0001251736,0.0001465458,0.00003864341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004617957,"about_ca_system_score_gemma":0.0003497936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01087594,"about_ca_topic_score_gemma":0.01112341,"domain_scores_codex":[0.9998388,0.00005329437,0.00001023359,0.00003414736,0.00004204781,0.0000214056],"domain_scores_gemma":[0.9996198,0.0002090318,0.00007795982,0.00001244794,0.00006385235,0.0000168321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001707975,0.001224582,0.08749993,0.0002916632,0.0002536304,0.000282967,0.0003036664,0.2898793,0.1834907,0.0005250652,0.0009982376,0.4335423],"study_design_scores_gemma":[0.00001961848,0.0002919761,0.08949558,0.000008628341,0.00004486118,0.0000344062,0.0001223464,0.8963885,0.01301983,0.0002875723,0.0002698785,0.00001676151],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984831,0.0002334918,0.01392382,0.00006368061,0.000005847661,0.00002585952,0.00006847548,0.00009694358,0.000750861],"genre_scores_gemma":[0.9892471,0.0001356375,0.01007447,0.00002038843,0.000006449636,0.00001842851,0.0001348698,0.000004258605,0.0003583663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01087594,"threshold_uncertainty_score":0.02162528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314259885454806,"score_gpt":0.2544060350729969,"score_spread":0.2229800465275163,"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."}}