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Record W4408467324 · doi:10.5194/egusphere-egu25-18780

Evaluating Total Cation Accounting (TCA) as an MRV Approach for Enhanced Rock Weathering - Insights from a trial in Ontario, Canada

2025· preprint· en· W4408467324 on OpenAlexaboutno aff
Amanda Stubbs, Rosalie Tostevin, Matthew Healey, Kirstine Skov, W. Turner, Giulia Cazzagon, T. Albahri, Tzara Bierowiec, Lucy Jones, Zoe Couillard, Josh Couillard, Courtney Stadtke, Gabrielle Janfield, Logan Wisteard, Declan Dejordy, Daniel Chaput, Xinran Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsWeatheringAccountingGeologyGeochemistrySoil scienceEconomics

Abstract

fetched live from OpenAlex

Enhanced rock weathering is a promising carbon dioxide removal (CDR) technology that involves the dissolution of silicate minerals (e.g., wollastonite). This process releases elements such as calcium, which can remain in solution and be charge balanced by bicarbonate, or be stored as pedogenic carbonate or on soil exchange sites. To verify carbon removal credits, robust monitoring, reporting, and verification (MRV) approaches are essential. In this study, we explore total cation accounting (TCA) as a novel method for MRV. TCA involves conducting a total digest of a soil-feedstock mixture in the near-field zone (NFZ; here defined as 15 cm) and analysing the major cation content via Inductively Coupled Plasma Optical Emission Spectroscopy. The resulting data include baseline cations in the soil (pre-spread), residual feedstock (post-spread), and weathered cations bound to exchange sites or forming carbonate minerals (post-spread). The major cations (Ca, Mg, K, Na) are summed to calculate total cations, and net cation loss from the NFZ is used to determine CDR.This study uses data from a small plot monitoring site (SPMS; 4x10 m) in Ontario, Canada, where Canadian Wollastonite feedstock was applied at four different densities (0, 5, 50, and 100 t/ha) in November 2023. Soil samples were collected across the SPMS using a 20:1 composite of 15 cm soil cores. The soils were finely crushed to preserve the distribution of larger feedstock particles, ensuring homogeneity and maintaining a representative soil-to-feedstock ratio.Prior to spreading, cation concentrations in both treatment and control groups were clustered around a similar mean, representing the baseline soil composition. Samples collected post-spread show an increase in cation content on treatment plots, demonstrating that the addition of our feedstock is resolvable, even in settings with unusually high background soil Ca content. All subsequent samples show cation content decreasing relative to the post-spread levels, indicating cations have been exported into the far-field zone (FFZ) or lost via plant uptake or solid transport. The results are consistent with other MRV approaches applied on the same field trial, although our results suggest that other methods can underestimate CDR. One advantage of TCA over competing MRV methods is that the results are time-integrative, meaning the signal-to-noise ratio improves with longer sampling intervals. These first field trials using this approach demonstrate its potential as a scalable, robust methodology for MRV in ERW trials.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.255
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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