Silicic acid biogeochemistry in the Gulf of California: Insights from sedimentary Si isotopes
Bibliographic record
Abstract
Iron is considered to play a large role in the cycling of Si in Fe‐limited regions of the ocean, but little is known about its role in Si biogeochemistry outside these areas. Here, we present published sediment trap data, new nutrient profiles and high resolution sedimentary records (Si isotopes, Biogenic silica%, N% and C%) from the Gulf of California, a non‐Fe‐limited region, to investigate the history of Si cycling in this highly productive basin. Modern nutrient profiles show that silicic acid in subsurface waters is in excess relative to nitrate and is therefore incompletely utilized during moderate winter upwelling events. Modern data, however, suggest that during intense upwelling episodes, silicic acid is preferentially utilized relative to nitrate by the biota, which we suggest reflects transient iron limitation. Our new δ30Si record from the Guaymas Basin shows dramatic variations at millennial timescales. Low δ30Si values synchronous with Heinrich events are interpreted as resulting from the decline in Si(OH)4 utilization at times of decreased upwelling strength, while nearly complete Si(OH)4 utilization was observed at times of invigorated upwelling and increased opal burial during the Holocene, the Bølling‐Allerød and the last glacial period. We attribute the complete utilization of Si(OH)4 to the occurrence of transient Fe limitation at these times. Our study highlights the importance of Fe limitation on Si and C cycling in coastal upwelling regions and suggests that upwelling dynamics, in combination with Fe availability, have the potential to modulate marine Si distribution and opal burial even at short timescales.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".