Nutrient-doped synthetic silicates for enhanced weathering, remineralization and fertilization on agricultural lands of global cold regions – A perspective on the research ahead
Bibliographic record
Abstract
There is now a dire demand for negative emissions technologies (which sequester CO 2 from the atmosphere) that can be rapidly deployed, are scalable, and are demonstrably safe and effective. Enhanced weathering of silicate minerals has demonstrated a significant potential for CO 2 capture and sequestration by the formation of pedogenic carbonates in soils, subsoils, and sediments. This technique has also been shown to deliver fruitful results in terms of improving soil health, and in turn plant health, through remineralization. The silicate minerals that possess the highest weathering rates (e.g., wollastonite), are relatively rare in nature, whereas the abundant ones (e.g., anorthite and forsterite) have a slower pace of weathering, especially in colder and drier climates such as found in the extensive agricultural lands of Western Canada and the Western United States. Herein, we offer a perspective on the opportunities for computational studies targeting atomic-scale interaction of CO 2 with silicates and synthesis of fast-weathering silicates (such as larnite and bredigite), whose composition can be tuned to also support soil fertilization and remineralization, and whose production must be integrated with green and carbon-neutral technologies to ensure net-negative life cycle emissions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".