{"id":"W3019680021","doi":"10.5194/bg-17-4059-2020","title":"Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico","year":2020,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Leidos; Gulf of Mexico Research Initiative","keywords":"Biogeochemical cycle; Argo; Phytoplankton; Environmental science; Ocean color; Chlorophyll a; Biogeochemistry; Oceanography; Atmospheric sciences; Satellite; Geology; Ecology; Chemistry; Biology","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":[0.0006036235,0.00009849716,0.0001613881,0.00004529697,0.0001275587,0.00007919051,0.0005714263,0.0000620115,0.00001983792],"category_scores_gemma":[0.0005207261,0.00005455895,0.00007550917,0.0007481311,0.0003109893,0.0002626402,0.00003107511,0.00006870936,6.318082e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002404677,"about_ca_system_score_gemma":0.0001310521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001502028,"about_ca_topic_score_gemma":0.0002112676,"domain_scores_codex":[0.9988632,0.00009218578,0.0003330577,0.0002194133,0.0003072775,0.0001849329],"domain_scores_gemma":[0.9987396,0.0008345933,0.0001780139,0.0001320805,0.00007032009,0.00004541848],"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":[0.0002233399,0.0001335594,0.7778669,0.0001593524,0.00002234843,0.000001261971,0.001795728,0.2034122,0.007326562,0.005329085,0.001554766,0.002174869],"study_design_scores_gemma":[0.0002482449,0.0001414578,0.006478962,0.0000175628,0.00001644172,0.000001222076,0.00188553,0.9842011,0.005737262,0.0007201165,0.0004681355,0.00008391293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907747,0.0001145498,0.003081392,0.003342832,0.000113414,0.0004529029,0.0007667029,0.0000140722,0.001339433],"genre_scores_gemma":[0.9936073,0.00001623611,0.005911448,0.0001715665,0.00004447703,0.000005140447,0.0002396669,0.000001783729,0.000002401511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.780789,"threshold_uncertainty_score":0.2270627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06406094478333224,"score_gpt":0.2662609532272404,"score_spread":0.2022000084439081,"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."}}