{"id":"W2941857669","doi":"10.3389/fmars.2019.00196","title":"Globally Consistent Quantitative Observations of Planktonic Ecosystems","year":2019,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":370,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Island University; Canadian Pacific Railway (Canada); Dalhousie University","funders":"Natural Environment Research Council; Centre National de la Recherche Scientifique; National Oceanic and Atmospheric Administration; Institut Universitaire de France; Sight Research UK; Cooperative Institute for the North Atlantic Region; Simons Foundation; National Aeronautics and Space Administration; National Science Foundation","keywords":"Plankton; Computer science; Marine ecosystem; Environmental science; Temporal scales; Ecosystem; Variety (cybernetics); Environmental resource management; Data science; Remote sensing; Ecology; Biology; Geography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009152552,0.0001169456,0.000281564,0.0002591485,0.00007442107,0.00005763874,0.0006233056,0.00003532752,0.0005926533],"category_scores_gemma":[0.0001013556,0.0001011826,0.00004800185,0.001184146,0.000218632,0.0004334308,0.0001083418,0.0001008357,0.00008970394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002352482,"about_ca_system_score_gemma":0.0003069841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005034233,"about_ca_topic_score_gemma":0.006420297,"domain_scores_codex":[0.9983546,0.00005154942,0.0003859234,0.0003486872,0.000514609,0.0003446779],"domain_scores_gemma":[0.99926,0.00006777393,0.0001659635,0.0003000672,0.000111295,0.00009489231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002541178,0.00001372128,0.9910932,0.00004494825,0.000006427356,0.000003133778,0.00006958783,0.001561465,0.0001162192,0.001595516,0.0004633833,0.005007045],"study_design_scores_gemma":[0.0002951793,0.0001887594,0.9315131,0.00004298049,0.000004339402,0.000007002672,0.000916645,0.0621517,0.00007104471,0.001263223,0.003393482,0.0001525781],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9252927,0.00007586742,0.0001751059,0.00008204194,0.001830283,0.0003291651,0.00005859741,0.00001817899,0.07213806],"genre_scores_gemma":[0.9783551,0.00002131405,0.0206519,0.0000572103,0.00001388915,0.000001670126,0.00003651382,0.000002045766,0.0008603458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07127771,"threshold_uncertainty_score":0.7610288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574245358667133,"score_gpt":0.2122050408706235,"score_spread":0.1964625872839522,"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."}}