{"id":"W2802387495","doi":"10.1111/sed.12488","title":"How turbidity current frequency and character varies down a fjord‐delta system: Combining direct monitoring, deposits and seismic data","year":2018,"lang":"en","type":"article","venue":"Sedimentology","topic":"Geological formations and processes","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Natural Resources Canada; Natural Environment Research Council; University of Southampton; Sight Research UK; Simon Fraser University; Transport Canada; Royal Roads University","keywords":"Geology; Turbidity current; Channelized; Turbidite; Bedform; Submarine pipeline; Current (fluid); Delta; Geomorphology; Submarine landslide; Channel (broadcasting); Submarine; Sediment; Ripple marks; Landslide; Oceanography; Structural basin; Sediment transport; Sedimentary depositional environment","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002095513,0.0001279259,0.0001986467,0.00005140834,0.0003074636,0.000167819,0.0002082074,0.00007605977,0.00005851691],"category_scores_gemma":[0.00004946682,0.00009278054,0.000009449051,0.00009389019,0.000224583,0.0005164427,0.00006030239,0.0001181254,0.00002770984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002485758,"about_ca_system_score_gemma":0.00002230244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008070705,"about_ca_topic_score_gemma":0.0005560162,"domain_scores_codex":[0.999105,0.00008016959,0.0001495982,0.0003044562,0.0001017178,0.0002590898],"domain_scores_gemma":[0.99944,0.0001030687,0.00008647209,0.0002148709,0.00005298617,0.0001026694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001506824,0.00001119046,0.9726034,0.000120568,0.00002394621,0.000005829666,0.0003397216,9.917306e-7,0.00001871737,0.0001819727,0.0001992662,0.02647933],"study_design_scores_gemma":[0.000374009,0.0003958981,0.9752011,0.00007898433,0.0000680013,0.0001793087,0.0003356632,0.003440362,0.0002610934,0.0007017886,0.01870663,0.0002571483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896386,0.006536045,0.0001849408,0.0006435236,0.001102404,0.0001294128,0.0001142511,0.00006301281,0.001587815],"genre_scores_gemma":[0.9988344,0.0002758174,0.0003426357,0.00005428369,0.0002203824,0.000001947134,0.0002285313,0.000001763513,0.00004029063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02622218,"threshold_uncertainty_score":0.3783481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03987767357653617,"score_gpt":0.2548101865964125,"score_spread":0.2149325130198763,"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."}}