{"id":"W4408436097","doi":"10.5194/egusphere-egu25-7319","title":"Using micro-computed tomography (&amp;#181;CT) to measure annually resolved sediment fluxes in varved sediments","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Geological formations and processes","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Varve; Sediment; Geology; Measure (data warehouse); Computed tomography; Tomography; Geomorphology; Radiology; Medicine; Computer science; Data mining","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":[0.0003628821,0.0004659253,0.0002218737,0.001070624,0.0002138833,0.0008746001,0.0005126093,0.0004196317,0.001176991],"category_scores_gemma":[0.0007870233,0.000408126,0.0002684805,0.001229819,0.000372564,0.0006223251,0.0004084432,0.0003577832,0.0003320856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000340424,"about_ca_system_score_gemma":0.0002964745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004615347,"about_ca_topic_score_gemma":0.01248464,"domain_scores_codex":[0.9998289,0.0000159875,0.00001611033,0.00005980587,0.00006495903,0.00001419146],"domain_scores_gemma":[0.9997103,0.00009311109,0.00007583216,0.00003725582,0.00006408562,0.00001943913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002837743,0.00008698698,0.1704924,0.0004716507,0.0002209472,0.0008498413,0.000563108,0.07189388,0.597123,0.002985237,0.001644928,0.1533843],"study_design_scores_gemma":[0.00002602833,0.0001693703,0.437144,0.0001328191,0.0001926747,0.001142617,0.0003328905,0.3150411,0.2335746,0.001766341,0.01036015,0.000117402],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7295107,0.0009076771,0.2582456,0.0001648776,0.00005325021,0.0001065484,0.00287629,0.001637072,0.006498055],"genre_scores_gemma":[0.7614191,0.00075389,0.2341099,0.00007496785,0.00001601039,0.0001072389,0.001021041,0.0002203037,0.002277572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004615347,"threshold_uncertainty_score":0.00917697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04839380505577658,"score_gpt":0.2665551658777607,"score_spread":0.2181613608219842,"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."}}