{"id":"W1967476626","doi":"10.1109/jstars.2013.2271583","title":"Testing the Top-Down Model Inversion Method of Estimating Leaf Reflectance Used to Retrieve Vegetation Biochemical Content Within Empirical Approaches","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia; Natural Resources Canada; University of Toronto","funders":"Canadian Space Agency; University of Victoria","keywords":"Hyperspectral imaging; Reflectivity; Remote sensing; Chlorophyll; Inversion (geology); Computer science; Mathematics; Environmental science; Artificial intelligence; Algorithm; Botany; Geology; Physics; Biology; Optics","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":[0.002494972,0.0005808832,0.0003296986,0.0003398236,0.0003199339,0.0006182406,0.0008049806,0.0004946869,0.001006786],"category_scores_gemma":[0.006854472,0.0002477549,0.0006338611,0.0002316162,0.0003121194,0.00094854,0.0006880469,0.0007813962,0.0003086434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006073279,"about_ca_system_score_gemma":0.001452514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02020182,"about_ca_topic_score_gemma":0.0115762,"domain_scores_codex":[0.9991686,0.0003239918,0.00004384218,0.0001698897,0.0002223171,0.00007137725],"domain_scores_gemma":[0.997291,0.001567747,0.0001418464,0.0003323723,0.0006269063,0.00004007327],"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":[0.0006489836,0.0005864653,0.02487059,0.0001610603,0.0003769653,0.0001552149,0.000189234,0.7850997,0.02858133,0.003756071,0.0006299053,0.1549445],"study_design_scores_gemma":[0.00001679008,0.00009065154,0.001852356,0.000003444728,0.00001910009,0.00001673474,0.00002445941,0.9929548,0.004617994,0.0002076509,0.0001865599,0.000009517017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7033812,0.0001095616,0.2911644,0.0001838197,0.00003284956,0.0001021171,0.0002565685,0.001265133,0.003504342],"genre_scores_gemma":[0.8949775,0.00006032559,0.1034658,0.0000517028,0.000009932524,0.00005317463,0.000306747,0.0001402399,0.0009345536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02020182,"threshold_uncertainty_score":0.0401684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230493636894185,"score_gpt":0.278338799839608,"score_spread":0.1552894361501894,"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."}}