{"id":"W4223656045","doi":"10.5194/essd-2022-51","title":"A global marine particle size distribution dataset obtained with the Underwater Vision Profiler 5","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Université Laval","funders":"Horizon 2020 Framework Programme; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; Sorbonne Université; Nuclear Safety and Security Commission; Deutsche Forschungsgemeinschaft; National Kidney Foundation of South Africa; European Commission; Agence Nationale de la Recherche; M.J. Murdock Charitable Trust; National Aeronautics and Space Administration; National Science Foundation","keywords":"Range (aeronautics); Particle-size distribution; Particle (ecology); Underwater; Particle size; Sampling (signal processing); Oceanic basin; Environmental science; Geology; Remote sensing; Oceanography; Physics; Materials science; Geomorphology; Structural basin; Optics; Paleontology; Detector","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":[0.0004575366,0.001298694,0.0009416959,0.002270147,0.0003638222,0.0006540166,0.001089048,0.0008971475,0.002417212],"category_scores_gemma":[0.001178,0.000306234,0.0009147767,0.001921869,0.000206557,0.0006580966,0.001083691,0.000791383,0.005065388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004871717,"about_ca_system_score_gemma":0.0006470661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01558655,"about_ca_topic_score_gemma":0.0250791,"domain_scores_codex":[0.9993435,0.00004640168,0.00007268456,0.0001904645,0.0002754716,0.00007151601],"domain_scores_gemma":[0.9992015,0.00006279386,0.000101832,0.0001443407,0.0004222401,0.00006728439],"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":[0.0009582784,0.00076237,0.160978,0.002529012,0.001079197,0.001273049,0.0003611673,0.01544356,0.04227727,0.001174215,0.5855184,0.1876455],"study_design_scores_gemma":[0.0002908548,0.0003360883,0.6787559,0.0003846801,0.0001921736,0.0005855617,0.0004554529,0.05262036,0.01903089,0.0009921581,0.2461322,0.0002236873],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07373677,0.0004165876,0.005922437,0.0001460189,0.0001356102,0.0002393398,0.9102718,0.004588764,0.004542674],"genre_scores_gemma":[0.04250397,0.0001236393,0.010572,0.00006319778,0.00002620553,0.0002216694,0.9453902,0.000140598,0.0009585769],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01558655,"threshold_uncertainty_score":0.03099167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025026277414373,"score_gpt":0.2215425669424346,"score_spread":0.2112923041682908,"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."}}