Bibliographic record
Abstract
There are microorganisms in soils and their activity can have a positive or negative impact on soil properties and groundwater. The positive effect of microorganisms includes the self-purification and self-regulation of contaminated groundwater and soil. The negative effect on soil is the change in grain-size composition; the weakening of engineering properties of soils; and the development processes, such as biogas generation, microbial quick-sand formation, and soil liquefaction. This paper addresses the negative effects of microbial activity on soil. Research on the impact of microbial activity in an underground space has been motivated by observations associated with underground infrastructure, such as subway tunnels, utilities tunnels, deep mines including those with tailings, and infrastructure with shallow and deep foundations. An overview of microorganisms in soil and an analysis of microbial activity in soils under the influence of natural and human-made factors are presented. Field and laboratory experiments show the significant impact of microbial activity on the engineering properties and consistency of the soil. Due to the complexity of the process, a study of microbial activity in the soil profile requires an integration of microbiology, biochemistry, engineering geology, and geotechnical engineering knowledge and experience.
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 | 0.001 |
| 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 | 0.001 | 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".