{"id":"W1510898397","doi":"10.1002/rob.20405","title":"Persistent ocean monitoring with underwater gliders: Adapting sampling resolution","year":2011,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"U.S. Naval Research Laboratory; Office of Naval Research; Multidisciplinary University Research Initiative; Jet Propulsion Laboratory; University of Southern California Sea Grant, University of Southern California; U.S. Navy; California Ocean Protection Council; California Institute of Technology; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Underwater glider; Underwater; Sampling (signal processing); Environmental science; Marine debris; Computer science; Oceanography; Marine engineering; Remote sensing; Geography; Geology; Debris; Engineering; Telecommunications","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":[0.0003259643,0.0001010622,0.0001365653,0.0000524682,0.0001013379,0.00003028028,0.0002736143,0.0000889087,0.00003453107],"category_scores_gemma":[0.00007625025,0.00007384198,0.00007625946,0.00009048702,0.00006508144,0.0002619737,0.0001290716,0.0003161208,0.000006860651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517045,"about_ca_system_score_gemma":0.000008274053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009034172,"about_ca_topic_score_gemma":0.000004352654,"domain_scores_codex":[0.9990516,0.00002888098,0.0002933557,0.0001088621,0.0002954247,0.0002218523],"domain_scores_gemma":[0.999459,0.0000582649,0.0002239314,0.0001714241,0.00002662096,0.00006070941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002280374,0.0001871535,0.7466007,0.00004079501,0.0001687859,0.0001582174,0.007067648,0.2331203,0.00703914,0.0001248042,0.0006236685,0.004640726],"study_design_scores_gemma":[0.004497706,0.01436097,0.2732304,0.003267764,0.001142528,0.002386133,0.07283249,0.006766371,0.599539,0.01566812,0.003200046,0.00310841],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453029,0.00006362887,0.0522965,0.0009726516,0.0004909461,0.00005984453,1.577212e-7,0.00005309348,0.0007602755],"genre_scores_gemma":[0.8098867,0.00001929999,0.1899023,0.00001914023,0.0001112927,1.748012e-7,5.410334e-8,0.000008475925,0.00005255507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5924999,"threshold_uncertainty_score":0.3011188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114232897304535,"score_gpt":0.2695390559518539,"score_spread":0.1553061586473189,"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."}}