Oceanic distribution of inorganic germanium relative to silicon: Germanium discrimination by diatoms
Bibliographic record
Abstract
Seventeen inorganic germanium and silicon concentration profiles collected from the Atlantic, southwest Pacific, and Southern oceans are presented. A plot of germanium concentration versus silicon concentration produced a near‐linear line with a slope of 0.760 × 10−6 (±0.004) and an intercept of 1.27 (±0.24) pmol L−1 (r2 = 0.993, p < 0.001). When the germanium‐to‐silicon ratios (Ge/Si) were plotted versus depth and/or silicon concentrations, higher values are observed in surface waters (low in silicon) and decreased with depth (high in silicon). Germanium‐to‐silicon ratios in diatoms (0.608–1.03 × 10−6) and coupled seawater samples (0.471–7.46 × 10−6) collected from the Southern Ocean are also presented and show clear evidence for Ge/Si fractionation between the water and opal phases. Using a 10 box model (based on PANDORA), Ge/Si fractionation was modeled using three assumptions: (1) no fractionation, (2) fractionation using a constant distribution coefficient (KD) between the water and solid phase, and (3) fractionation simulated using Michaelis‐Menten uptake kinetics for germanium and silicon via the silicon uptake system. Model runs indicated that only Ge/Si fractionation based on differences in the Michaelis‐Menten uptake kinetics for germanium and silicon can adequately describe the data. The model output using this fractionation process produced a near linear line with a slope of 0.76 × 10−6 and an intercept of 0.92 (±0.28) pmol L−1, thus reflecting the oceanic data set. This result indicates that Ge/Si fractionation in the global ocean occurs as a result of subtle differences in the uptake of germanium and silicon via diatoms in surface waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".