{"id":"W2962943868","doi":"10.5194/bg-17-475-2020","title":"A global end-member approach to derive <i>a</i> <sub>CDOM</sub> (440) from near-surface optical measurements","year":2020,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Takuvik Joint International Laboratory; Université Laval","funders":"Japan Aerospace Exploration Agency; National Aeronautics and Space Administration","keywords":"Colored dissolved organic matter; Arctic; Turbidity; Environmental science; Water quality; Attenuation; Attenuation coefficient; Inversion (geology); Surface water; Wavelength; Remote sensing; Geology; Physics; Optics; Chemistry; Oceanography; Nutrient","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001410162,0.001133164,0.0005739379,0.002089909,0.0005668447,0.001048455,0.0008652552,0.0005761071,0.001157411],"category_scores_gemma":[0.00174054,0.0003207985,0.001006627,0.001185721,0.0004240185,0.001013095,0.001457835,0.000686541,0.0007921099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004856074,"about_ca_system_score_gemma":0.0009703836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003847853,"about_ca_topic_score_gemma":0.005688266,"domain_scores_codex":[0.9995734,0.00008075737,0.00002938773,0.0001657331,0.0001126491,0.00003805653],"domain_scores_gemma":[0.9993401,0.00009804886,0.0001013957,0.0001274937,0.0003034646,0.00002955356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003186906,0.000381486,0.06242475,0.0002357921,0.0006796279,0.0001015494,0.0004894119,0.2074101,0.1156607,0.006752641,0.002400558,0.6031447],"study_design_scores_gemma":[0.00003885002,0.0001994656,0.03706412,0.00003596667,0.0002564346,0.0001061998,0.0002928877,0.8905694,0.05671046,0.005602827,0.009041414,0.00008193596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06871312,0.00009759216,0.9274068,0.00004964192,0.00002330694,0.00008978752,0.0003498277,0.001098502,0.002171484],"genre_scores_gemma":[0.2919882,0.00007959105,0.7036961,0.00007352339,0.00002386941,0.0001577006,0.001996716,0.000324153,0.001660275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003847853,"threshold_uncertainty_score":0.007650912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03858779180054463,"score_gpt":0.2153218832882393,"score_spread":0.1767340914876947,"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."}}