{"id":"W3075397214","doi":"10.3390/rs12162659","title":"Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Toronto","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Multispectral image; Precision agriculture; Remote sensing; Computer science; Environmental science; Agriculture; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001251814,0.0008738134,0.0008074708,0.002618443,0.0004239464,0.001357076,0.0007346415,0.0009864517,0.005561841],"category_scores_gemma":[0.001485554,0.0003819595,0.0007989263,0.004113827,0.0005076912,0.002227881,0.000909435,0.001264162,0.002693416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005564168,"about_ca_system_score_gemma":0.0009101511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009340812,"about_ca_topic_score_gemma":0.0008810632,"domain_scores_codex":[0.9991498,0.0001034436,0.00008225728,0.0001661647,0.0004348817,0.00006346258],"domain_scores_gemma":[0.9985796,0.0004555943,0.0001718654,0.00005772278,0.0006703004,0.00006491587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001263866,0.00008997631,0.001176324,0.01624261,0.0001025558,0.0003861371,0.000227493,0.001429789,0.03294631,0.007487676,0.02823719,0.9115475],"study_design_scores_gemma":[0.000007942808,0.0001759584,0.002899401,0.001724802,0.0001753081,0.001378736,0.0001797639,0.001696177,0.0182509,0.004664341,0.9687663,0.00008038602],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003321106,0.9715995,0.008752712,0.0009562601,0.0009725713,0.00003212485,0.0001518199,0.0001398647,0.01407411],"genre_scores_gemma":[0.0173971,0.9663342,0.008497676,0.0008404208,0.001356323,0.00003329928,0.000356647,0.00004220079,0.005142185],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005561841,"threshold_uncertainty_score":0.01860619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005420909667225058,"score_gpt":0.2099816887241409,"score_spread":0.2045607790569159,"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."}}