Reply to Schuiling et al.: Different processes at work
Bibliographic record
Abstract
Schuiling et al.(1) question our conclusion (2) that the annual dissolution rate of olivine is limited by the saturation of waters with silicic acid (H 4 SiO 4 ), which is one product of the dissolution reaction of olivine.In support of this point they discuss findings of CO 2 sequestration in a mine in Yukon, Canada, claiming that a minimum of 1,700 g C m -2 y -1 was sequestered between 1978 and 2004 by silicate weathering and precipitation of (mainly) magnesium carbonates (3).This value is approximately 20 times larger than the 85 g C m -2 y -1 calculated in our study for the Amazon basin (2).How to explain this discrepancy?According to the reaction scheme ( Eq. 1 in ref2), olivine weathering leads to production of alkalinity and silicic acid.Thus, we asked how much alkalinity and silicic acid can leave catchment areas (open systems) via rivers per year.This amount is limited for a given amount of water discharge by (i) the change in pH due to addition of alkalinity, and (ii) the solubility of silicic acid affecting the dissolution rate of olivine.For the Amazon this would allow a maximum CO 2 sequestration of 4.4 Pg C y -1 (restricting the pH to 9.0) and only 0.5 Pg C y -1 due to the solubility limit of silicic acid.From the latter limit and the size of the catchment area, we calculated the mean sequestration rate of 85 g C m -2 y -1 .We do not claim that this value gives the maximum rate at a single location.In contrast, Schuiling et al. (1) refer to a different set of processes.Although they also start with silicate weathering, it is
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.056 | 0.067 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".