ADVANCING ANTARCTIC SEDIMENT CHRONOLOGY THROUGH COMBINED RAMPED PYROLYSIS OXIDATION AND PYROLYSIS-GC-MS
Bibliographic record
Abstract
ABSTRACT Radiocarbon ( 14 C) dating of sediment deposition around Antarctica is often challenging due to heterogeneity in sources and ages of organic carbon in the sediment. Chemical and thermochemical techniques have been used to separate organic carbon when microfossils are not present. These techniques generally improve on bulk sediment dates, but they necessitate assumptions about the age spectra of specific molecules or compound classes and about the chemical heterogeneity of thermochemical separations. To address this, the Rafter Radiocarbon Laboratory has established parallel ramped pyrolysis oxidation (RPO) and ramped pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) systems to thermochemically separate distinct carbon fractions, diagnose the chemical composition of each fraction, and target suitable RPO fractions for radiocarbon dating. Three case studies of sediment taken from locations around Antarctica are presented to demonstrate the implementation of combined RPO-AMS and Py-GC-MS to provide more robust age determination in detrital sediment stratigraphy. These three depositional environments are good examples of analytical and interpretive challenges related to oceanographic conditions, carbon sources, and other factors. Using parallel RPO-AMS and Py-GC-MS analyses, we reduce the number of radiocarbon measurements required, minimize run times, provide context for unexpected 14 C ages, and better support interpretations of radiocarbon measurements in the context of environmental reconstruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".