Effects of microorganism within organic matter on the mechanical behaviour of solidified municipal dredged mud
Bibliographic record
Abstract
Municipal mud consists of organic matter naturally deposited in a microbial-rich environment, and its common pre-treatment in the laboratory is normally different from that in situ. In this study, an improved pre-loading method and the common pre-treatment method (by air or oven drying) were first applied to investigate the effect of microorganisms within organic matter on performance of the solidified soils. Results reveal that (i) Atterberg limits in the pre-loading method were higher than those in the drying method; (ii) the time-dependent strength became stable for the solidified soils pre-treated by the drying method, while strength decreased for the soils pre-treated by the pre-loading method; (iii) pH value of solidified soils by the pre-loading method decreased more significantly. After excluding the possible porosity influence on solidified soils, the effects of microorganisms within organic matter were investigated by microbial identification tests, including fluorescence detection and high-throughput sequencing. The pre-treatment procedure changed the vitality and diversity of microorganisms, leading to a rebalance between acid erosion and cement hydration during long-term curing. At the end, the long-term strength of the solidified municipal mud by the traditional pre-treatment method (by air or oven drying) could be overevaluated.
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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".