Geochemical Drivers of Enhanced Rock Weathering in Soils
Bibliographic record
Abstract
Experimental research indicates that the efficiency of enhanced rock weathering (ERW) as a form of carbon dioxide removal (CDR), is subject to large variations in effectivity, and that the current state of knowledge is not sufficient to develop robust predictive capabilities. It appears that the heterogeneous mineralogy and reactivity of basalt, as well as the regional and local pedoclimatic parameters, greatly influence its weathering characteristics, and in turn, its ability to sequester CO 2 . Therefore, ERW efficiency should not be taken for granted but should, rather, be pursued according to a careful rock and soil geochemical selection. If ERW is to eventually become a significant CDR technology, then future research programs must bring together the fields of geochemistry, engineering, life cycle analysis, biology, soil physics, hydrology, and agronomy, as well as social sciences such as economy, law, and sociology. Furthermore, it is vitally important that research constraints relating to CDR methodologies be lifted in the immediate future. Indeed, CDR funds need to be allocated based on solid science to insure their overall efficiency as well as the credibility of the scientific community in the long run.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".