{"id":"W2161700255","doi":"10.1109/lgrs.2004.833776","title":"Narrowband Vegetation Indexes and Detection of Disease Damage in Soybeans","year":2004,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","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":"Spectroradiometer; Narrowband; Sclerotinia sclerotiorum; Chlorophyll; Vegetation (pathology); Environmental science; Biology; Horticulture; Canopy; Reflectivity; Sclerotinia; Remote sensing; Botany; Agronomy; Medicine; Optics; Geology; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001978715,0.0001185847,0.0001134799,0.00007402354,0.0001374755,0.00003951114,0.00005717014,0.00004798139,5.992989e-7],"category_scores_gemma":[0.00003827099,0.00009663562,0.00002157266,0.0003476248,0.0005966272,0.0002535498,0.0000336581,0.0001220752,0.000003175837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007571174,"about_ca_system_score_gemma":0.000008276879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651455,"about_ca_topic_score_gemma":0.001363078,"domain_scores_codex":[0.9989978,0.00003924292,0.000162498,0.0003367255,0.0002473524,0.0002163602],"domain_scores_gemma":[0.999665,0.00002398007,0.0000797055,0.0001276767,0.000006665236,0.00009692663],"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":[0.00001310263,0.000008104414,0.001796377,0.00001695449,0.000001236327,0.0000259152,0.001203893,0.005000629,0.9465207,8.553997e-7,0.000007930795,0.04540429],"study_design_scores_gemma":[0.0006462903,0.00006071264,0.9070477,0.0002646582,0.00001775183,0.00006681181,0.0001705699,0.02781632,0.06263316,0.0008893809,0.00007453514,0.0003120701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895064,0.00002660235,0.008803448,0.001263542,0.0001797396,0.0001219076,5.026337e-7,0.00001825598,0.0000795969],"genre_scores_gemma":[0.993322,0.00002699548,0.00602666,0.0005729753,0.00002490017,1.507446e-8,5.907821e-7,0.000005901134,0.00002000388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9052514,"threshold_uncertainty_score":0.3940686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005037604728864773,"score_gpt":0.193901703587793,"score_spread":0.1888640988589283,"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."}}