Controls on biogenic silica burial in the Southern Ocean
Bibliographic record
Abstract
Abstract Understanding the controls on opal export in the Southern Ocean can inform both the prediction of how the leakage of silicic acid from the Southern Ocean responds to climate and the interpretation of paleo‐proxies. We have compiled a database of 185 230Thorium‐normalized opal burial rates and 493 opal concentration measurements in Southern Ocean sediments and matched these with environmental climatologies. By subdividing the Southern Ocean on the basis of oceanographic regions and interpolating the opal burial rates, we estimate a total biogenic Si burial south of 40°S of 2.3 ± 1.0 Tmol Si yr−1. In both the seasonally ice‐covered and permanently ice‐free regions we can explain 73% of opal burial variability from surface ocean properties. Where sea ice is present for at least part of the year, the length of the ice‐free season determines the upper limit of opal burial in the underlying sediments. In the ice‐free regions of the Southern Ocean, the supply of silicic acid through winter mixing is the most important factor. Our results do not support a strong role of iron in controlling opal burial. We do however find that satellite‐derived net primary production increases with increasing (modeled) dust delivery. These findings support the decoupling between carbon and opal fluxes in the Southern Ocean. When corrected for opal dissolution, the observed opal fluxes are in reasonable agreement with fluxes simulated using an ocean biogeochemical model. However, the results suggest current preservation algorithms for opal could be improved by incorporating the composition of particle flux, not only its magnitude.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
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".