{"id":"W2988264169","doi":"10.3390/rs11222609","title":"Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Université Laval","funders":"Marine Environmental Observation Prediction and Response Network; Fisheries and Oceans Canada; National Aeronautics and Space Administration","keywords":"SeaWiFS; Environmental science; Visible Infrared Imaging Radiometer Suite; Ocean color; Remote sensing; Chlorophyll a; Satellite; Moderate-resolution imaging spectroradiometer; Oceanography; Phytoplankton; Biogeochemical cycle; Radiometer; Spectroradiometer; Reflectivity; Geology; Physics; Environmental chemistry; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123285,0.00006096735,0.00009412711,0.00006517952,0.00004619392,0.00005095969,0.00004234966,0.00003748807,0.00004324818],"category_scores_gemma":[0.00005142995,0.0000461626,0.00001501774,0.0002526806,0.00001409848,0.00004865444,0.000001913547,0.00006074971,0.0000297787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001781975,"about_ca_system_score_gemma":0.0001984857,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4247274,"about_ca_topic_score_gemma":0.7759537,"domain_scores_codex":[0.9990402,0.0001729605,0.0001440593,0.0001334874,0.0003561263,0.00015317],"domain_scores_gemma":[0.9996098,0.00007339306,0.0000429159,0.0001200732,0.0000915058,0.00006229722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001796794,0.000001670565,0.1086192,0.00002461745,0.000004932819,0.000007851056,0.0008178175,0.00338006,0.0001623346,0.000005630235,0.00001125505,0.8869466],"study_design_scores_gemma":[0.0001923022,0.00006463015,0.1219296,0.00004656658,0.00001262629,0.000009205069,0.00044415,0.8761265,0.0001038837,0.0000884848,0.0009121089,0.00006990988],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883899,0.0001069372,0.00005180276,0.000348137,0.000118994,0.0003774567,0.000005704083,0.000003670693,0.0105974],"genre_scores_gemma":[0.9995333,0.00001068736,0.0002439333,0.0001178256,0.00002996177,2.340277e-9,0.00004722354,0.000001638963,0.00001544789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8868767,"threshold_uncertainty_score":0.5791035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897656425398107,"score_gpt":0.2185808491361029,"score_spread":0.1996042848821218,"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."}}