Experimental and numerical investigation of microparticle transport and deposition in the context of permafrost thaw
Bibliographic record
Abstract
In natural porous environments, soil particle migration during flow plays an important role in soil stability and pollutant transport by affecting soil mechanical properties and water quality. In northern areas, permafrost degradation alters the subsurface connection pathways leading to mass movements and rearrangement of the soil. To date, few models have included the influence of temporal and spatial variations of flow velocity and porous media heterogeneity on the transport and deposition of suspended particles. In this study, laboratory column experiments and a numerical model were used to investigate these issues. The laboratory column experiments were carried out under different flow rates and the effect of porous media heterogeneity was investigated using different grain size distributions. The soil columns were reconstituted from several samples taken in the studied site, the Tasiapik Valley, located in the discontinuous permafrost zone near Umiujaq, Nunavik, Québec. During the experiments, the spatio-temporal distribution of the porosity and the hydraulic conductivity was monitored using X-ray computed tomography imaging (CT-SCAN). Using the pore water velocity computed from the groundwater flow solution, the advection–dispersion transport equation with a first-order kinetic term for particle deposition was solved using the finite element model Heatflow/Smoker. The dependency of the attachment kinetics on the pore water velocity and on the porous media heterogeneity was included. The model was tested and validated with an analytical solution and calibrated with the experimental data. Our simulations highlight the roles of hydrodynamic conditions and soil characteristics on particle transport and deposition mechanisms and the susceptibility of the porous medium to thermo-suffosion in permafrost environments.
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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 | 0.000 | 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".