{"id":"W4410410730","doi":"10.1029/2024pa005083","title":"Late Pleistocene Sediment Provenance and Paleoenvironmental Changes in the East Siberian Shelf Margin: Insights From Mineralogical and Nd Isotope Analysis","year":2025,"lang":"en","type":"article","venue":"Paleoceanography and Paleoclimatology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Oceans and Fisheries; Korea Institute of Marine Science and Technology promotion; Korea Polar Research Institute; National Research Foundation","keywords":"Provenance; Geology; Pleistocene; Margin (machine learning); Geochemistry; Sediment; Paleontology; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038077,0.0003398518,0.0006594801,0.0005867817,0.0004144492,0.00005701372,0.0002675076,0.0003246356,0.000185817],"category_scores_gemma":[0.00002751875,0.0002384919,0.00008782116,0.0008963234,0.001336763,0.0001106879,0.00006739934,0.0004445415,0.000008381773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":8.495351e-7,"about_ca_system_score_gemma":0.00001539431,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001469362,"about_ca_topic_score_gemma":0.04109757,"domain_scores_codex":[0.997432,0.0006539344,0.000363175,0.0007820189,0.0001719401,0.0005969355],"domain_scores_gemma":[0.9986581,0.0007719648,0.0001062731,0.0003080435,0.00001555248,0.0001400789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002127215,0.00005718736,0.9966056,0.00006183918,0.0002595265,0.0001411557,0.001052061,0.00001194789,0.00001309328,0.0006734601,0.00001696385,0.0008944942],"study_design_scores_gemma":[0.000896649,0.0001970521,0.9924414,0.00002784125,0.0002326096,0.00006649653,0.0007534396,0.001247645,0.000009628464,0.003336425,0.0005453038,0.0002455123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733579,0.01972591,0.00001224801,0.003097716,0.0001113066,0.0004764714,0.00009005558,0.00002270307,0.003105685],"genre_scores_gemma":[0.9945765,0.003811719,0.0002221309,0.001071737,0.00002627868,0.00002694986,0.0002271173,0.000003159147,0.00003439515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04095064,"threshold_uncertainty_score":0.9763999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0094067527143754,"score_gpt":0.209945775790998,"score_spread":0.2005390230766226,"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."}}