{"id":"W4391651947","doi":"10.1017/rdc.2023.116","title":"ADVANCING ANTARCTIC SEDIMENT CHRONOLOGY THROUGH COMBINED RAMPED PYROLYSIS OXIDATION AND PYROLYSIS-GC-MS","year":2024,"lang":"en","type":"article","venue":"Radiocarbon","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Ministerio de Ciencia e Innovación; Ministry of Science and Innovation, New Zealand; Ministry of Oceans and Fisheries; Ministry of Business, Innovation and Employment; Korea Polar Research Institute; European Commission","keywords":"Chronology; Radiocarbon dating; Pyrolysis; Sediment; Geology; Accelerator mass spectrometry; Environmental chemistry; Gas chromatography–mass spectrometry; Mass spectrometry; Mineralogy; Oceanography; Geochemistry; Paleontology; Chemistry; Chromatography; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002119727,0.0002673401,0.0003893466,0.0002893921,0.0000848269,0.00012052,0.0001210725,0.0001498576,0.0001267697],"category_scores_gemma":[0.00002541487,0.0002522831,0.0001393051,0.0007003207,0.0000627387,0.0003811846,0.00003625952,0.0002564437,0.00005397654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319213,"about_ca_system_score_gemma":0.00003061829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396468,"about_ca_topic_score_gemma":0.00008674546,"domain_scores_codex":[0.9985448,0.00006414707,0.0003767678,0.0003979864,0.0002458816,0.0003704558],"domain_scores_gemma":[0.999419,0.00008387466,0.00003307484,0.0003227956,0.00002442554,0.000116841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001028303,0.0001607211,0.001173385,0.001146336,0.0039573,0.0002181537,0.009817911,0.290439,0.6644311,0.002878001,0.003933756,0.0217415],"study_design_scores_gemma":[0.0006762914,0.0001092519,0.0003009925,0.00008270712,0.000420287,0.00001917036,0.0003246636,0.9547047,0.03222945,0.0007854411,0.009920535,0.000426514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661682,0.009191046,0.0186671,0.001682195,0.0007357279,0.0002540745,0.000003323039,0.001066404,0.002231955],"genre_scores_gemma":[0.9965377,0.002219974,0.000598756,0.0001154702,0.0002027941,0.00005082981,0.00007289928,0.00006192046,0.0001396153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6642657,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006726582880697492,"score_gpt":0.223987182485083,"score_spread":0.2172605996043855,"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."}}