{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004620566,0.0001145307,0.0001594235,0.00004217248,0.00005396125,0.00001076865,0.00007964653,0.00006453965,0.000007573733],"category_scores_gemma":[0.00005324088,0.00008736654,0.00002183126,0.0009572214,0.0001675778,0.00008892344,0.0000935867,0.0001841794,0.00001048342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007147767,"about_ca_system_score_gemma":0.000003922137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002433075,"about_ca_topic_score_gemma":0.00004508963,"domain_scores_codex":[0.9991909,0.00002295457,0.0001708752,0.0003000874,0.0001306751,0.0001844734],"domain_scores_gemma":[0.9997073,0.00002128729,0.00008135485,0.0001149926,0.00001399581,0.00006099962],"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":[0.000003906604,0.000007281743,0.00253435,0.00001032936,0.000002553169,0.00001052831,0.0004783032,0.001134597,0.3405245,0.00003854708,0.0001103955,0.6551447],"study_design_scores_gemma":[0.001566029,0.0001504496,0.05121236,0.0003612848,0.00009387083,0.0007851336,0.01308463,0.1538318,0.2513298,0.009162052,0.5168325,0.001590074],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9586993,0.004845326,0.006042523,0.01349834,0.00005833785,0.0005269911,0.00000309615,0.0001630132,0.01616308],"genre_scores_gemma":[0.9350873,0.001494652,0.06296648,0.00032268,0.00007852907,1.468515e-8,0.0000055205,0.00001457502,0.00003025017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6535546,"threshold_uncertainty_score":0.3562704,"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."}}