{"id":"W4221088376","doi":"10.5194/bg-19-1705-2022","title":"Improved prediction of dimethyl sulfide (DMS) distributions in the northeast subarctic Pacific using machine-learning algorithms","year":2022,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimethyl sulfide; Subarctic climate; Mesoscale meteorology; Environmental science; Sea surface temperature; Dimethylsulfoniopropionate; Climatology; Atmospheric sciences; Nutrient; Geology; Oceanography; Chemistry; Phytoplankton","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":[0.001195265,0.0008085037,0.0005482761,0.0006948536,0.0002989013,0.000564507,0.0005919284,0.0005815482,0.000414884],"category_scores_gemma":[0.002033655,0.0002998801,0.0006250627,0.0004967888,0.0002030381,0.0005480664,0.0003182716,0.0006381849,0.0001226765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004091,"about_ca_system_score_gemma":0.001005699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04402747,"about_ca_topic_score_gemma":0.02648194,"domain_scores_codex":[0.9998407,0.00005292322,0.00001079651,0.00005660786,0.00001857746,0.0000202879],"domain_scores_gemma":[0.9991738,0.0004262734,0.0001153106,0.00005178002,0.0001894732,0.00004324402],"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":[0.00003851081,0.00006819303,0.02461499,0.00001071221,0.00006538962,0.00002869749,0.000009395819,0.961822,0.000562986,0.0001051628,0.0002788979,0.01239516],"study_design_scores_gemma":[0.000001196582,0.000002434141,0.0009188571,5.363769e-7,0.000001774564,8.605975e-7,0.000001028838,0.9989632,0.00006967729,0.0000270487,0.00001239351,8.829338e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457638,0.0003423348,0.05169543,0.0002575853,0.00004243283,0.0000186065,0.0004315899,0.0006125457,0.0008358231],"genre_scores_gemma":[0.985623,0.00006091316,0.01352004,0.00003303192,0.00001746197,0.00001296654,0.0004561059,0.00001726136,0.0002590324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04402747,"threshold_uncertainty_score":0.08754241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01845474497771164,"score_gpt":0.2170337739367773,"score_spread":0.1985790289590656,"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."}}