Dynamics of silicon metabolism and silicon isotopic discrimination in a marine diatomas a function of pCO<sub>2</sub>
Bibliographic record
Abstract
Opal accumulation rates in sediments have been used as a proxy for carbon flux, but there is poor understanding of the factors that regulate the Si quota of diatoms. Natural variation in silicon isotopes (δ30Si) in diatom frustules recovered from sediment cores are an alternative to opal mass for reconstructing diatom Si use and potential C export over geological timescales. Understanding the physiological factors that may influence the Si quota and the δ30Si isotopic signal is vital for interpreting biogenic silica as a paleoproxy. We investigated the influence of pCO2 on the Si quota, fluxes across the cell membrane, and frustule dissolution in the marine diatom Thalassiosira weissflogii and determined the effect that pCO2 has on the isotopic fractionation of Si. We found that our Si flux estimates mass balance and, for the first time, describe the Si budget of a diatom. The Si quota rose in cells grown with low pCO2 (100 ppm) compared with controls (370 ppm), and the increased quota was the result of greater retention of Si (i.e., lower losses of Si through efflux and dissolution). The ratio of efflux : influx decreased twofold as pCO2 decreased from 750 to 100 ppm. The efflux of silicon is shown to significantly bias measurements of silica dissolution rates determined by isotope dilution, but no effect on the Si isotopic enrichment factor (ε) was observed. The latter effect suggests that silicon isotopic discrimination in diatoms is set by the Si transport step rather than by the polymerization step. This observation supports the use of the v signal of biogenic silica as an indicator of the percentage utilization of silicic acid.
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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 | 0.000 | 0.000 |
| 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".