Techniques for porewater extraction from clayrocks
Bibliographic record
Abstract
The chemical and isotopic composition of porewaters in clayrocks records the palaeo-hydrogeological evolution of sedimentary basins. However, porewater extraction is in general demanding and time-consuming. Techniques have been newly developed or adapted in recent years, and progress in analytical methods, in particular the minimisation of sample mass needed for analysis, have opened new opportunities for porewater studies. Depending on their degree of induration, clayrocks span a wide range of porosities. They all have in common a nanometric pore-space architecture, which results in low permeability and the predominance of diffusive solute transport through the formation. The negatively charged clay-mineral surfaces affect both the chemical composition and the binding state of the water molecules in the adjacent pore space. Thus, in a profile across a pore, the composition and the mobility of the porewater vary as a function of the distance from the clay surface. In a simplified way, two water types, namely free, charge-balanced porewater in the central parts of a pore and bound/interlayer water along clay surfaces can be distinguished. Adequate field sampling protocols are needed to preserve core materials from evaporation and oxidation, and to this end well-trained on-site staff is a pre-requisite in order to minimise exposure of the samples to the atmosphere. Porewater extraction from clayrocks requires dedicated methods that are based on different physical principles, including the application of high hydraulic gradients (advective displacement, centrifugation, sampling of in-situ seepages), axial pressure (squeezing), diffusive equilibration (out-diffusion, isotope diffusive exchange, in-situ circulation tests), capillary suction (filter absorption), heating (vacuum distillation), crushing and dilution (aqueous extraction), and outgassing via diffusion (analysis of dissolved noble and reactive gases). Different extraction methods often yield consistent results, but distinct differences may occur if the methods sample different domains of the pore space. The attribution of water extracted by a specific method to a particular porewater reservoir is not always evident, and current research aims at a better understanding of this issue. The suitability of a particular method depends on the properties of the studied clayrock (porosity, degree of cementation, mineralogy) and the parameters of interest (major ions, stable water isotopes, dissolved gases). Each method has its limitations and incurs various kinds of artefacts that may require corrections. It is concluded that there is no single best method to extract the porewater and to analyse the dissolved constituents and isotope composition, and that a method or combination of methods should be selected considering the properties of the studied clayrock.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".