{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002617443,0.001462598,0.0005954797,0.001289188,0.001291831,0.001354498,0.002052841,0.0008247934,0.0008673713],"category_scores_gemma":[0.0045866,0.0003890729,0.0007801184,0.001589867,0.0005159914,0.0005994179,0.0006299924,0.0005462198,0.0002801698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008585575,"about_ca_system_score_gemma":0.0104055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9255627,"about_ca_topic_score_gemma":0.9012948,"domain_scores_codex":[0.999056,0.0000728278,0.00008010952,0.000280842,0.0003827315,0.0001274211],"domain_scores_gemma":[0.997775,0.0003068566,0.0001172894,0.0001220734,0.001561634,0.0001171811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001337487,0.0007033338,0.2452308,0.000332963,0.001478464,0.0002861307,0.000360331,0.4698761,0.02795981,0.001033869,0.005263509,0.2461371],"study_design_scores_gemma":[0.0002377836,0.0001604579,0.153358,0.00003351105,0.0002466376,0.00007050098,0.0002666855,0.8318955,0.01059545,0.0001133971,0.002940163,0.00008194402],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824499,0.0008478479,0.008638577,0.0002558013,0.00008634112,0.0001709215,0.002002026,0.001289783,0.004258844],"genre_scores_gemma":[0.9660497,0.0003783095,0.02596042,0.0001375733,0.00001651414,0.00005070074,0.005545686,0.0001042972,0.001756639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07443732,"threshold_uncertainty_score":0.1497514,"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."}}