Global Ocean Sediment Composition and Burial Flux in the Deep Sea
Bibliographic record
Abstract
Quantitative knowledge about the burial of sedimentary components at the seafloor has wide-ranging implications in ocean science, from global climate to continental weathering. The use of 230 Th-normalized fluxes reduces uncertainties that many prior studies faced by accounting for the effects of sediment redistribution by bottom currents and minimizing the impact of age model uncertainty. Here we employ a recently compiled global dataset of 230 Th-normalized fluxes with an updated database of seafloor surface sediment composition to derive global maps of the burial flux of calcium carbonate, biogenic opal, total organic carbon (TOC), non-biogenic material, iron, mercury, and excess barium (Baxs). The spatial patterns of burial of the major components are mainly consistent with prior work, but the new quantitative estimates allow evaluations of global deep-sea burial. Our integrated deep-sea burial fluxes are 136 Tg C/yr CaCO3, 153 Tg Si/yr opal, 20Tg C/yr TOC, 220 Mg Hg/yr, and 2.6 Tg Baxs/yr. Sedimentary Fe fluxes reflect a mixture of sources including lithogenic material, hydrothermal inputs and authigenic phases. The fluxes of some commonly used paleo-productivity proxies (TOC, biogenic opal, and Baxs) are not well-correlated geographically with satellite-based productivity estimates. Our new compilation of sedimentary fluxes provides more detailed information on burial fluxes, which should lead to improvements in the understanding of how preservation affects these paleoproxies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".