{"id":"W2922140667","doi":"10.5194/essd-11-355-2019","title":"A rare intercomparison of nutrient analysis at sea: lessons learned and recommendations to enhance comparability of open-ocean nutrient data","year":2019,"lang":"en","type":"article","venue":"Earth system science data","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Ocean Frontier Institute; Dalhousie University; European Commission","keywords":"Comparability; Nutrient; Environmental science; Transect; Data quality; Oceanography; Ocean chemistry; Data set; Computer science; Seawater; Ecology; Mathematics; Geology; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003720397,0.000165673,0.0006764666,0.0003162931,0.0002330729,0.0002136114,0.004514281,0.00003591088,0.0004258766],"category_scores_gemma":[0.0001984601,0.0001371494,0.00003787205,0.002371771,0.0002034139,0.001395082,0.003445955,0.0001030655,0.0001051601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001447912,"about_ca_system_score_gemma":0.0001902216,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01162058,"about_ca_topic_score_gemma":0.02851758,"domain_scores_codex":[0.9968417,0.0002301667,0.0007418929,0.001200875,0.0006335689,0.000351852],"domain_scores_gemma":[0.9953142,0.0002674981,0.000432077,0.003539505,0.0001691249,0.0002775721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009225051,0.0001134657,0.9857321,0.0003877931,0.0000990178,0.000001560955,0.0005751765,0.0007902972,0.0002769842,0.0005554416,0.003565275,0.007810669],"study_design_scores_gemma":[0.0006878236,0.0004976708,0.3783951,0.0005990752,0.0002483815,0.0000190228,0.007320352,0.5617709,0.002883329,0.00004978432,0.04706709,0.0004615749],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806126,0.0001599491,0.001396086,0.0005544913,0.0002936184,0.0009383424,0.01319716,0.00002037107,0.002827401],"genre_scores_gemma":[0.9952167,0.00002444573,0.0009735754,0.00002228659,0.00001524789,0.00000134443,0.003521696,0.000002582607,0.0002220854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.607337,"threshold_uncertainty_score":0.9949611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08900344309832835,"score_gpt":0.3491962496752463,"score_spread":0.2601928065769179,"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."}}