{"id":"W2589592374","doi":"10.3389/fmars.2017.00041","title":"A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean","year":2017,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Bedford Institute of Oceanography","funders":"National Centre for Earth Observation; Japan Aerospace Exploration Agency; Natural Environment Research Council; Sociedad Española de Oncología Médica; Sight Research UK; National Aeronautics and Space Administration","keywords":"Ocean color; Satellite; Phytoplankton; Computer science; Remote sensing; Environmental science; Resource (disambiguation); Radiometry; Algorithm; Geography; Ecology; Engineering","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":[0.001794858,0.00233245,0.001725329,0.004733646,0.00082252,0.001865094,0.002744778,0.002748627,0.187823],"category_scores_gemma":[0.007305582,0.001234835,0.00129071,0.005839958,0.0009177759,0.002939349,0.001676156,0.003162634,0.1477538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000877771,"about_ca_system_score_gemma":0.002390047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288353,"about_ca_topic_score_gemma":0.02397646,"domain_scores_codex":[0.9989938,0.0001883131,0.0001562815,0.0001496946,0.0004644155,0.00004750442],"domain_scores_gemma":[0.9952853,0.002122456,0.0002140579,0.0003461406,0.001797149,0.0002348582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004603369,0.00009646891,0.0003784875,0.0008492981,0.00001846783,0.0001691763,0.0001077492,0.0005618196,0.001432265,0.002056492,0.8136989,0.1805847],"study_design_scores_gemma":[0.00001346537,0.00003706579,0.001129989,0.0005085413,0.000009061984,0.0002155385,0.00005836883,0.0004142281,0.0001740464,0.002979649,0.9944367,0.00002337113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002481891,0.1207356,0.2389178,0.02996907,0.01488768,0.004142414,0.08816876,0.02701367,0.4736831],"genre_scores_gemma":[0.008368852,0.09445696,0.3366691,0.0315583,0.004529539,0.003647539,0.04383782,0.007891322,0.4690406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.187823,"threshold_uncertainty_score":0.6283307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009969141928863312,"score_gpt":0.2328294694910934,"score_spread":0.2228603275622301,"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."}}