{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007406966,0.0004463947,0.000530232,0.0004566144,0.0001997343,0.0004562061,0.000794557,0.0004575743,0.0001975727],"category_scores_gemma":[0.002392812,0.0002741579,0.0001884099,0.000311341,0.0003797201,0.0005856962,0.0007057381,0.0004884674,0.00006384351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000346851,"about_ca_system_score_gemma":0.0004262483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004756382,"about_ca_topic_score_gemma":0.00433683,"domain_scores_codex":[0.9997459,0.00004952797,0.00001560875,0.00006681435,0.0000789357,0.00004319919],"domain_scores_gemma":[0.9988617,0.0005058316,0.0001500035,0.0002036591,0.0002231206,0.00005571656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002921807,0.0001621064,0.01691804,0.00004625021,0.00007723671,0.0001129142,0.0002234716,0.7270874,0.05265611,0.000924008,0.0004776925,0.2010225],"study_design_scores_gemma":[0.00002018706,0.00009535623,0.001747457,0.000003063564,0.000009178241,0.0000185011,0.00003132083,0.9929695,0.004672458,0.0002576227,0.0001687237,0.000006608986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6745449,0.0001244835,0.3237863,0.00009034157,0.00001848452,0.00004757706,0.00003025403,0.0007405505,0.0006169805],"genre_scores_gemma":[0.920689,0.00002682586,0.07900507,0.00001497806,0.000005043825,0.00002332061,0.00003234512,0.00002697864,0.0001765065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004756382,"threshold_uncertainty_score":0.009457409,"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."}}