Characterizing effects of mechanical compaction on macropores of reclaimed soil using computed tomography scanning
Bibliographic record
Abstract
To study the effects of mechanical compaction on soil macropore structure in the process of reclamation, this study investigated reclaimed soil mechanically compacted in coal mining area with high groundwater level. The computed tomography scanning technology was employed to get soil slice images, and ArcGIS® was used to analyze the porosity, number, size, morphology, and distribution of macropores in reclaimed soil, at different compaction times (0, 1, 3, 5, 7, and 9) and depths (0–20, 20–40, and 40–60 cm). Our results proved that the mechanical compaction could decrease the macroporosity, number, and equivalent diameter (ED) of the macropores in reclaimed soil, while the morphology became rounded. The distribution of macropore ED was steeper and more asymmetrical than macropore circularity. However, mechanical compaction could make them flat and symmetrical. The macroporosity, number, ED, and circularity of macropores in packing layer (40–60 cm) were smaller than backfilling top layer (0–20 cm) and sublayer (20–40 cm). Thus, we suggest that subsoiling in packing layer is responsible for the improved macropore characteristics. Moreover, these macropores will possess better permeability if the compaction times are controlled.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".