{"id":"W4239624180","doi":"10.3389/fmars.2020.581790","title":"GO-SHIP Repeat Hydrography Nutrient Manual: The Precise and Accurate Determination of Dissolved Inorganic Nutrients in Seawater, Using Continuous Flow Analysis Methods","year":2020,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Agency for Marine-Earth Science and Technology; National Research Council Canada; Natural Environment Research Council; Sight Research UK; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Hydrography; Seawater; Nutrient; Certified reference materials; Environmental science; Nitrate; Process engineering; Computer science; Oceanography; Chemistry; Geology; Detection limit; Ecology; Engineering; Chromatography; Biology","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":[0.002192022,0.0001647427,0.0003878845,0.0003790101,0.0001465003,0.00009441645,0.0006096945,0.00004630458,0.00001926401],"category_scores_gemma":[0.0002494318,0.0001256412,0.00009998748,0.005437103,0.0007304652,0.000427501,0.0007406159,0.0001642786,0.00000119745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552145,"about_ca_system_score_gemma":0.00001493283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007090706,"about_ca_topic_score_gemma":0.00002425619,"domain_scores_codex":[0.9976622,0.0003511031,0.0004824533,0.0006136543,0.0005271079,0.0003634197],"domain_scores_gemma":[0.9992241,0.00006361651,0.0002031518,0.0003500895,0.00002299111,0.0001360832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003128052,0.0000603579,0.9624478,0.00001270277,0.00002004531,0.000004356131,0.002296559,0.002267482,0.0131923,0.000001708471,0.000009286283,0.01965616],"study_design_scores_gemma":[0.0005588866,0.00007395399,0.5628737,0.000026443,0.0002325583,0.000001368598,0.001431798,0.3572242,0.07606973,0.001147125,0.00007011594,0.00029009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525085,0.00002672139,0.04686982,0.0001878613,0.0001291078,0.00020415,0.000002890962,0.00001442233,0.00005651275],"genre_scores_gemma":[0.8709776,0.00003604503,0.1288996,0.0000234083,0.0000135445,0.000008255254,0.000003640131,0.000006442086,0.00003145874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3995741,"threshold_uncertainty_score":0.5123498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197773539011599,"score_gpt":0.3014452185137397,"score_spread":0.2794674831236237,"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."}}