An emulation-based approach for interrogating reactive transport models
Bibliographic record
Abstract
Abstract. We present an emulation-based approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilise a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions, and 2 biologically mediated reactions; this RTM describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially mediated sulfate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".