{"id":"W2915081206","doi":"10.5194/essd-11-1037-2019","title":"A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two","year":2019,"lang":"en","type":"article","venue":"Earth system science data","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"National Marine Fisheries Service; Office of Polar Programs; Institut national des sciences de l'Univers; Natural Environment Research Council; Fisheries and Oceans Canada; University of California, San Diego; California Department of Fish and Wildlife; Sorbonne Université; Centre National de la Recherche Scientifique; Centre National d’Etudes Spatiales; National Oceanic and Atmospheric Administration; Sight Research UK; National Science Foundation; European Space Agency; National Aeronautics and Space Administration","keywords":"Metadata; Computer science; Satellite; Remote sensing; Environmental science; Data quality; Information retrieval; Meteorology; Geology; Geography; Service (business); World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001850912,0.0001033076,0.0002197275,0.00008459642,0.0001148265,0.00009734739,0.00207491,0.00003777428,0.00007520445],"category_scores_gemma":[0.00007135635,0.00008771601,0.00001858179,0.0009221181,0.0001150576,0.001122393,0.000369346,0.00005458085,0.0003842004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001288538,"about_ca_system_score_gemma":0.000304634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252007,"about_ca_topic_score_gemma":0.009000259,"domain_scores_codex":[0.9981302,0.00004343094,0.0003789395,0.0006760707,0.0004628394,0.0003085033],"domain_scores_gemma":[0.9977789,0.0001257935,0.0001537398,0.00172894,0.00007538919,0.0001372243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006977912,0.00003733199,0.960483,0.0003487699,0.000007117686,0.000002071475,0.00002571731,0.000625033,0.0005579893,0.002435575,0.0004172791,0.03499034],"study_design_scores_gemma":[0.0008335561,0.0001500552,0.4808988,0.0001477039,0.0000178124,0.0000269839,0.0006944545,0.4711893,0.00009146752,0.00005955693,0.04564825,0.0002419814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.888795,0.0004245262,0.004598653,0.0002564183,0.001067309,0.003598343,0.05532857,0.00009685979,0.04583432],"genre_scores_gemma":[0.9895464,0.000005977906,0.001545173,0.00001966254,0.0000557129,0.00000130809,0.008780641,0.000001680459,0.00004344699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4795842,"threshold_uncertainty_score":0.5022356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924333921223938,"score_gpt":0.2689524936692376,"score_spread":0.2397091544569983,"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."}}