{"id":"W2205156897","doi":"10.3390/rs71215875","title":"Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Eddy covariance; Covariance; Canopy; Environmental science; Photosynthetically active radiation; Primary production; Distributed ray tracing; Atmospheric sciences; Ray tracing (physics); Meteorology; Computer science; Remote sensing; Mathematics; Ecosystem; Statistics; Photosynthesis; Geography; Physics; Ecology; Optics; Botany","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":[0.0004877195,0.0004943614,0.0003919775,0.0004258351,0.0003461553,0.0005507371,0.0007650013,0.0006492046,0.0009505692],"category_scores_gemma":[0.001731188,0.0003146293,0.000514662,0.0006576088,0.0004196237,0.0004177245,0.0003010342,0.0005793154,0.000124517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102819,"about_ca_system_score_gemma":0.0009254537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03497449,"about_ca_topic_score_gemma":0.02746281,"domain_scores_codex":[0.9998798,0.00003153263,0.000008474267,0.00002810892,0.00003493651,0.00001708061],"domain_scores_gemma":[0.9992701,0.0004652966,0.0000752979,0.0000603733,0.00009861682,0.00003025895],"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":[0.00001252166,0.00001246406,0.001132483,0.000005301981,0.000007052066,0.00001189367,0.00001479763,0.996136,0.0006659971,0.0007364667,0.00004665714,0.001218253],"study_design_scores_gemma":[0.000002288251,0.000003098943,0.0001175881,4.049494e-7,9.117665e-7,0.000001795494,0.000001865557,0.9994717,0.0001881699,0.000166498,0.00004388017,0.000001692542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5809049,0.00008631617,0.4136409,0.0001688316,0.00006106017,0.00009094383,0.0005932067,0.001304995,0.003148743],"genre_scores_gemma":[0.9355831,0.00007854893,0.0624832,0.00003220885,0.00001308012,0.0000849225,0.0005166659,0.00009524374,0.001113132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03497449,"threshold_uncertainty_score":0.06954181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02078100628777685,"score_gpt":0.2293015554611172,"score_spread":0.2085205491733404,"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."}}