Correlative isotope excursions driven by transport, not global environmental change
Bibliographic record
Abstract
Carbonate sediments are widely used to reconstruct Earth's climatic and biogeochemical history. While pristine records come from deep-water settings with continuous vertical accumulation, much of the preserved carbonate record originates from shallow platforms, where horizontal transport, erosion, and diagenesis complicate this archive. We develop a numerical sediment transport model tracking the advection of conservative tracers (e.g., δ 13 C values of carbonate sediments) to explore the implications of redistribution on sedimentary archives. Results suggest that correlations between δ 13 C excursions and their vertical and lateral proximity to sequence boundaries can be explained by erosion and redeposition of older, isotopically distinct carbonate sediments, forming coarse-grained deposits above erosive surfaces. Such isotopic variability may reflect spatial gradients in the dissolved inorganic carbon (DIC) reservoir, with excursion magnitude constrained by the strength of those gradients. Alternatively, these excursions may record redeposition of older sediments, capturing past secular changes in δ 13 C D I C . We demonstrate that isotope excursions can be duplicated through redeposition, whereby initial shifts in DIC are re-expressed within younger stratigraphy with dampened magnitude. Applying these findings to the Hirnantian carbon isotope excursion (HICE) in Anticosti Island strata, we demonstrate that spatial isotopic gradients within basins will be recorded as excursions within progradational stratigraphy. These excursions will appear synchronous, despite being decoupled from global DIC change. Similar-magnitude δ 13 C excursions in coeval strata are often interpreted as evidence for global carbon cycle changes. We show that transport alone can produce such patterns, without invoking global mechanisms. Further, mapping intra-basin δ 13 C variability may help distinguish transport from global signals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".