Metal corrosion in partially saturated sands: pore fluid conductivity and water saturation
Bibliographic record
Abstract
Sediments can facilitate the electrochemical processes that drive the corrosion of buried metallic components. Common analyses and guidelines emphasize the effect of pore fluid conductivity on soil corrosivity and overlook the effect of partial saturation; yet, most buried metals are situated within the vadose zone. The detailed experimental study reported herein used mass loss measurements from passive corrosion tests, X-ray microtomography, and image analyses to examine the evolution of corrosion in C-steel coupons embedded in sand specimens mixed with de-ionized water and brine at various degrees of saturation. Experimental observations show that corroding cells preferentially form at contacting grains, and that the evolution of corrosion is biased by the variability in packing density and saturation, while fluid conductivity plays a lesser role. Above all, results highlight the critical importance of percolating gas and water phases, and show that water–grain–metal interfaces restrict the actively corroding area to a fraction of the entire metal surface. A complementary macroscale analysis anticipates asymptotic conditions based on mass and charge conservation, and the transport of corroding agents and residuals. Together, the measurements and model results highlight the significant impact of the degree of saturation on corrosion rates and mass loss.
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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) | 0.000 | 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 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".