{"id":"W1596190217","doi":"10.1002/lom3.10039","title":"Measurement of <scp>DMS</scp>, <scp>DMSO</scp>, and <scp>DMSP</scp> in natural waters by automated sequential chemical analysis","year":2015,"lang":"en","type":"article","venue":"Limnology and Oceanography Methods","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Woods Hole Oceanographic Institution; National Science Foundation","keywords":"Dimethylsulfoniopropionate; Dimethyl sulfide; Sulfur; Chemistry; Seawater; Sulfur cycle; Methanethiol; Sampling (signal processing); Transect; Environmental chemistry; Detection limit; Environmental science; Chromatography; Oceanography; Detector; Geology; Organic 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":[0.000361215,0.0003900733,0.00021282,0.0006396253,0.0002760582,0.0002587336,0.0003801153,0.0002121029,0.0006025665],"category_scores_gemma":[0.0004852821,0.0001819596,0.0001787203,0.0004430681,0.0003648035,0.0003117979,0.0004083204,0.0003047879,0.0002357905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004771259,"about_ca_system_score_gemma":0.0006527773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00395487,"about_ca_topic_score_gemma":0.005761601,"domain_scores_codex":[0.9993525,0.00005915492,0.00002308327,0.0001361128,0.0004039583,0.00002511716],"domain_scores_gemma":[0.9997054,0.00005463501,0.00006675458,0.00002260302,0.0001326801,0.00001803986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004174931,0.00003357538,0.004111221,0.00004373121,0.00001608171,0.00001557297,0.00002104848,0.0005289642,0.9792475,0.00008217074,0.0002035779,0.01565467],"study_design_scores_gemma":[0.00002042134,0.0002562347,0.01153114,0.000005839963,0.0000162731,0.00007431538,0.00003894273,0.0147884,0.9707773,0.00009947684,0.002370134,0.00002151229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9067144,0.0003338284,0.08796354,0.0001100901,0.0000320939,0.0002529447,0.001060999,0.001098959,0.002433232],"genre_scores_gemma":[0.8141309,0.0005291629,0.1815149,0.00009364307,0.00002988805,0.0004384873,0.0009333841,0.00007692987,0.002252719],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00395487,"threshold_uncertainty_score":0.0078637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324619170341726,"score_gpt":0.2720805978082242,"score_spread":0.248834406104807,"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."}}