{"id":"W2465659380","doi":"10.1016/j.rse.2016.05.006","title":"A new algorithm for discriminating water sources from space: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institut national des sciences de l'Univers; Japan Aerospace Exploration Agency; Centre National d’Etudes Spatiales; Networks of Centres of Excellence of Canada; Canada Research Chairs; Agence Nationale de la Recherche; European Space Agency; National Aeronautics and Space Administration","keywords":"Colored dissolved organic matter; Seawater; Biogeochemical cycle; Ocean color; Environmental science; Arctic; Surface water; Satellite; Water mass; Salinity; Sea ice; Remote sensing; Precipitation; Geology; Oceanography; Meteorology","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":[0.0004266918,0.0001535334,0.0002004035,0.00001927304,0.0003710316,0.00003311979,0.0001392258,0.00004313735,0.00002895883],"category_scores_gemma":[0.00003885755,0.00007717389,0.00004603847,0.00001593676,0.0001379229,0.00009823943,0.00009488031,0.0000539059,0.000002044051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001179562,"about_ca_system_score_gemma":0.00002226251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03044449,"about_ca_topic_score_gemma":0.001494546,"domain_scores_codex":[0.9989358,0.00005040989,0.0002320917,0.0003455831,0.0001714029,0.0002647216],"domain_scores_gemma":[0.9987724,0.0006373998,0.0001159305,0.000377994,0.000009870351,0.00008636426],"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":[0.00006664964,0.00002517601,0.01435346,0.00001727397,0.0001447019,0.00004600717,0.005953877,0.001099149,0.000241891,2.305167e-7,0.00002067768,0.9780309],"study_design_scores_gemma":[0.0008281977,0.000192606,0.001552196,0.00004205248,0.0003351332,0.0001099491,0.020379,0.9756976,0.0001173035,0.0003262584,0.0002556047,0.0001641398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6263059,0.00003250322,0.3722695,0.0002671946,0.00005315148,0.0004519832,0.0006122141,0.000005853679,0.000001663965],"genre_scores_gemma":[0.8325252,0.00001613607,0.1671158,0.00003391052,0.0001343902,3.369455e-8,0.00008285441,0.000009631945,0.00008200228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9778668,"threshold_uncertainty_score":0.9760119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223300098795808,"score_gpt":0.2459982327826812,"score_spread":0.2037652317947232,"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."}}