{"id":"W2808638327","doi":"10.5194/bg-15-3497-2018","title":"Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales","year":2018,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Takuvik Joint International Laboratory; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Centre National de la Recherche Scientifique; Agència de Gestió d'Ajuts Universitaris i de Recerca; Generalitat de Catalunya; ArcticNet; National Aeronautics and Space Administration","keywords":"Dimethylsulfoniopropionate; Environmental science; Biome; Satellite; Climatology; Dimethyl sulfide; Aerosol; Plankton; Oceanography; Atmospheric sciences; Meteorology; Phytoplankton; Geology; Geography; Ecosystem; Biology; Chemistry; Ecology; Sulfur","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":[0.000291949,0.0004429627,0.0002199601,0.0006363204,0.00008672273,0.0004476447,0.0002132817,0.0001877752,0.001134023],"category_scores_gemma":[0.0006272419,0.0001285095,0.0004652942,0.001039826,0.00009136958,0.0003918339,0.000349225,0.0002032416,0.0004602425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003074709,"about_ca_system_score_gemma":0.0003129892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103174,"about_ca_topic_score_gemma":0.01539418,"domain_scores_codex":[0.9999037,0.00001455328,0.000009300737,0.00003708428,0.00002675139,0.000008733418],"domain_scores_gemma":[0.9997839,0.00003125081,0.00006013408,0.0000439968,0.00006887868,0.00001170279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002332081,0.00007171893,0.7161396,0.0005639269,0.001012453,0.00020106,0.0001388647,0.06248103,0.05393205,0.0007744078,0.006076258,0.1583754],"study_design_scores_gemma":[0.00002975742,0.00006086153,0.8764379,0.00006351065,0.000254794,0.0000960094,0.0001632621,0.08477369,0.02506624,0.000855312,0.01215344,0.00004514605],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606004,0.001024377,0.01216009,0.0002094827,0.00004852585,0.0000351956,0.01949329,0.0006721087,0.005756435],"genre_scores_gemma":[0.974843,0.0003777973,0.01041533,0.00004386667,0.0000203613,0.00001797617,0.01351791,0.00006215719,0.0007015629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0103174,"threshold_uncertainty_score":0.02051473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03614473789764123,"score_gpt":0.253210966944466,"score_spread":0.2170662290468248,"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."}}