Quantifying Carbon Dioxide Removal using Pore Water Data from Enhanced Rock Weathering Field Trials in Scotland
Bibliographic record
Abstract
Enhanced rock weathering (ERW) has emerged as a promising carbon dioxide removal (CDR) strategy, with tens of dedicated EW commercial entities having been set up in the last three years. UNDO is one of these, with commercial operations across the Northern United Kingdom and Eastern Canada.Commercial entities selling CDR, like UNDO, are required to measure, verify and report how much CO2 has been removed at regular time intervals. However, different methods of CDR quantification are likely to produce different numbers, depending on where the measurement has taken place (e.g., soil-based measurement vs a pore-water sample). We propose an approach that uses pore water concentrations in conjunction with climate data to more robustly estimate CDR per unit area of land, as new pore water data is generated. This approach allows us to estimate the amount of charge-balanced bicarbonate/carbonate ions that are transported past a certain depth point. Furthermore, our method is compared against other indicators of weathering processes, such as exchangeable cations and carbonate precipitation. For these calculations and comparisons, we will use data from a field trial that has been running for 2 years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".