Revisiting the relationship between the pore water carbon isotope gradient and bottom water oxygen concentrations
Bibliographic record
Abstract
Reconstructing the oxygen content at the ocean floor provides insight into ocean circulation, ventilation, and carbon storage in the deep sea. The microbial breakdown of organic carbon within marine sediment through aerobic respiration consumes oxygen in the pore fluid and releases dissolved inorganic carbon. The offset in the carbon isotopic composition, δ13C, of epifaunal and infaunal foraminifera, Δδ13C, is considered to reflect the aerobic respiration of organic carbon and can be used to reconstruct the oxygen content of the bottom water. Previous work provided an empirical calibration that was suggested to be valid for oxygen reconstructions between 55–235 μmol kg-1. In this study, we apply a biogeochemical reactive transport model (RTM) to extend and update this calibration, allowing for the reconstruction of oxygen concentrations ([O2]) up to ∼300 μmol kg-1. Using the RTM and new bottom water [O2] and pore fluid measurements from Iberian Margin sediment cores, we also demonstrate that the calibration between the Δδ13C and bottom water [O2] must account for the coupled changes in the carbon system due to the respiration of organic carbon in the overlying ocean including the concentration and carbon isotopic composition of the dissolved inorganic carbon, and the δ13C of the organic carbon within the sediment column. We apply the improved calibration to reconstruct the changes in oxygen content at International Ocean Discovery Program (IODP) Site U1385 in the deep North Atlantic over the past 1.5 Myr.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".