The relative importance of grain size and mineral weatherability for enhanced rock weathering rates: a comparison of glacial rock flour and basaltic feedstocks
Bibliographic record
Abstract
There is currently no consensus in the literature as to whether the mineralogical composition and theoretical weatherability or grain size and surface area of feedstock materials used for carbon capture via enhanced rock weathering are stronger determinants of weathering rates of silicate minerals applied to agricultural soils. Felsic source rocks have typically been discounted for enhanced rock weathering in favor of more easily weatherable mafic and ultramafic rocks. However, previous work has indicated that Greenlandic glacial rock flour, a potential feedstock with an exceedingly fine grain size (d50 = 2.6 µm) but a felsic composition, can weather at sufficiently rapid rates to be effective for carbon capture and improving crop yields through the release of nutrients during weathering. Here we present initial experimental results comparing the use of Greenlandic glacial rock flour and several sources of basaltic material as feedstocks for enhanced rock weathering. Two field trials installed in Ghana and South Carolina demonstrate the varying effects of these materials on maize yield, and two flow-through laboratory experiments, one with plants and one without, assess the differences in alkalinity generation and cation release between these feedstocks over time. Among the tested basalts, chemical composition seems to be a stronger driver of weathering rates than differences in grain size. However, none were as finely ground as the glacial rock flour, which was found to weather at a rate comparable to or, in some cases, higher than the basaltic materials, despite being composed of less chemically reactive minerals. These results suggest that mineral weatherability is an important predictor of weathering rates, but with a large enough difference in grain size the amount of surface area available for reaction can be equally important.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".