{"id":"W2619624483","doi":"10.1002/2017gl073426","title":"An Argo mixed layer climatology and database","year":2017,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":381,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; National Natural Science Foundation of China","keywords":"Argo; Mixed layer; Climatology; Layer (electronics); Database; Geology; Meteorology; Environmental science; Computer science; Geography; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009059259,0.0007350064,0.0006968254,0.003865622,0.0003626326,0.00136835,0.001083908,0.0005659512,0.008636507],"category_scores_gemma":[0.00166907,0.000356639,0.0004112704,0.004664525,0.0001677287,0.0009328182,0.0005907124,0.0005207721,0.007610906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006524168,"about_ca_system_score_gemma":0.001004246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01740426,"about_ca_topic_score_gemma":0.01244656,"domain_scores_codex":[0.9994346,0.00005444916,0.0001121503,0.0001438744,0.0002002457,0.00005466149],"domain_scores_gemma":[0.9985157,0.0001186969,0.000249256,0.0003648139,0.000566923,0.0001845639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000999248,0.0005176027,0.1583957,0.001168029,0.0005142447,0.0007953006,0.0002839295,0.04341568,0.01172356,0.005261729,0.6262574,0.1506676],"study_design_scores_gemma":[0.0006740901,0.0001369419,0.2703798,0.0002703708,0.0001720273,0.0003706492,0.0003977822,0.08387395,0.01076641,0.003400078,0.6293265,0.0002314556],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04701161,0.0002245953,0.006253222,0.0001586354,0.00007805126,0.0002441219,0.9296607,0.00532599,0.01104299],"genre_scores_gemma":[0.0602448,0.0001248739,0.01513777,0.00007240439,0.00003612279,0.0002773914,0.92199,0.0003686855,0.001747951],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01740426,"threshold_uncertainty_score":0.03460592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219957411106854,"score_gpt":0.3287462891151968,"score_spread":0.2765467150041283,"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."}}