Incorporating enhanced rock weathering into sustainable forest management
Bibliographic record
Abstract
Enhanced rock weathering (ERW) can be implemented in managed forests that use selective harvesting through trail networks for carbon dioxide removal (CDR) while improving soil health by neutralizing excess acidity and restoring base cations. Wollastonite-rich rock powder (Wo = 28.4 wt% and D 50 = 350 μm) was applied using a tractor and spreader from a trail in Haliburton Forest, Ontario, Canada, to evaluate the practicability and challenges of incorporating ERW into silviculture practices. The intended amendment dosage of 5 t/ha aimed to replace soil Ca losses due to historic acidic deposition. Based on trail networks mapped in Haliburton Forest and a spreading width of 12 m, 85 % of the forest area could potentially be amended, assuming no overlapping areas. Spreading of 5 t/ha over the annually harvested area (∼700 ha) has a maximum potential to sequester 1120–1270 t CO 2 /yr based on the CDR potential of the amendment (377–427 kg CO 2 /t), calculated using its bulk geochemical composition. Assuming the same trail coverage as in Haliburton Forest and a dosage of 5 t/ha wollastonite-rich amendment, managed forests undergoing selection harvest in the United States and Canada have a maximum potential to sequester 4.5–5.1 Mt CO 2 /yr. The CDR rate of forest ERW requires field-based monitoring and life-cycle assessments. This application test successfully showed the technical feasibility of incorporating ERW into forest management via trail networks. However, it also demonstrated considerable spatial heterogeneity in dosages (0.8–6.7 t/ha), varying by proximity to trail centers, obstructions such as large trees, and overlapping applications in dense trail networks, which challenges the accuracy and verifiability of carbon credits generated. Selecting monitoring sites near trail centers and away from large obstructions and trail junctions will help ensure dosages are representative of the larger application. Furthermore, we recommend verifying dosages to avoid over- or under-estimating CDR in future forest ERW studies. • Forests have great potential for CO 2 removal (CDR) via enhanced rock weathering (ERW). • Applying alkaline rock powders for ERW may also improve soil and forest health. • Inaccurate CDR quantification can result from amendment and dosage heterogeneity. • Dosages at monitoring sites must be representative of the overall average dosage. • Monitoring should be near the trail centers, away from obstructions and other trails.
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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 | 0.000 | 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 | 0.000 | 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".