Direct measurement of CO<sub>2</sub> fluxes into kimberlite residues and powdered rocks: Implications for enhanced weathering
Bibliographic record
Abstract
As part of De Beers' Project CarbonVault, kimberlite residues from Venetia Mine in South Africa, as well as powdered forsterite [Mg 2 SiO 4 ], serpentinite [Mg 3 Si 2 O 5 (OH) 4 ], wollastonite skarn [CaSiO 3 ], and 10 wt.% brucite [Mg(OH) 2 ] mixed with quartz sand, were tested as potential feedstocks for enhanced weathering (EW). The goals of this study were to examine parameters that affect CO 2 drawdown and how EW can be used to remove CO 2 at mines. Venetia generates 4.74 Mt of residues per year that are a valuable feedstock for EW. These residues vary in grain size from fine (<1 mm) to coarse (1-8 mm), have high surface areas (6.8-13.4 m 2 /g), and contain reactive mafic minerals including serpentine [Mg 3 Si 2 O 5 (OH) 4 ], diopside [CaMgSi 2 O 6 ], and clinochlore [Mg 5 Al(AlSi 3 O 10 )(OH) 8 ; 1]. An arid climate drives evaporation leading to mine waters being saturated with respect to calcite [2], which is likely a carbon sink, yet cannot be distinguished from primary calcite. Experiments utilized a CO 2 gas analyzer with flux chambers to directly measure CO 2 removal. Unweathered kimberlite residues achieved the greatest drawdown rate of -870 g CO 2 /m 2 /yr at 48% saturation, whereas fine and coarse residues, previously exposed to process water achieved fluxes of -150 and -160 g CO 2 /m 2 /yr at 60% saturation, respectively. Brucite mixed with quartz reached -2940 g CO 2 /m 2 /yr at 14% saturation, in comparison to forsterite, serpentinite, and wollastonite that achieved fluxes of -500, -260, and -190 g CO 2 /m 2 /yr, respectively, at higher saturations of 53-60%. Experiments demonstrate that mineralogical composition and reactivity have the greatest effect on EW rates, followed by water content which affects permeability. Total inorganic carbon increased in the brucite, wollastonite, and unweathered kimberlite indicating that CO 2 was stored via mineral trapping as opposed to solubility trapping, which dominated in the other experiments. Modifying the management practices at Venetia by increasing the exposure of unweathered residues, expanding total dispersal area, and creating optimal water saturation would lead to greater CO 2 removal.
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 | 0.000 | 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".