{"id":"W2169302805","doi":"","title":"Stress Detection in Crops with Hyperspectral Remote Sensing and Physical Simulation Models","year":2004,"lang":"en","type":"article","venue":"DIGITAL.CSIC (Spanish National Research Council (CSIC))","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Ciencia y Tecnología; York University","keywords":"Hyperspectral imaging; Remote sensing; Crown (dentistry); Environmental science; Multispectral image; Canopy; Leaf area index; Red edge; Vegetation (pathology); Geography; Botany; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001458583,0.0002513934,0.0002134199,0.0001114579,0.0003206018,0.0004990848,0.0001852527,0.0001318198,0.00001146504],"category_scores_gemma":[0.002241093,0.0002100163,0.00005237479,0.001029232,0.0005207788,0.001465129,0.0001633605,0.0006144423,0.00008444908],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008411424,"about_ca_system_score_gemma":0.0006128991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683433,"about_ca_topic_score_gemma":0.004711972,"domain_scores_codex":[0.9910814,0.00009333701,0.0002452352,0.0007286546,0.00723724,0.0006141637],"domain_scores_gemma":[0.9976583,0.0003856098,0.00007981483,0.0002259999,0.001425049,0.0002252056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001828861,0.0002039315,0.0001449707,0.0000189429,0.00002491199,0.0000807587,0.001976126,0.9209279,0.009257739,0.0003966351,0.0002152677,0.06656996],"study_design_scores_gemma":[0.001762256,0.0003476181,0.01369395,0.0001885258,0.00001035614,0.0001056868,0.0007516583,0.9029444,0.003480989,0.07454955,0.001585336,0.0005796095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9440178,0.00002766808,0.007287628,0.0004789089,0.00004277374,0.0004970952,0.00002938769,0.00008167799,0.0475371],"genre_scores_gemma":[0.9977595,0.00001242912,0.001600833,0.00006524754,0.0001668608,0.000001002108,0.00001778211,0.00003382184,0.0003425013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07415291,"threshold_uncertainty_score":0.9953951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06911206961298388,"score_gpt":0.2942538047118456,"score_spread":0.2251417350988617,"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."}}