Determination of transport parameters for heavy metal in residual compacted soil using two methodologies
Bibliographic record
Abstract
Values of the hydrodynamic dispersion coefficient and retardation factor obtained using the traditional and cumulative mass methods of column test analysis for zinc, manganese, and cadmium in a compacted soil are compared. The soil under study is from the B horizon of a residual gneissic tropical soil used for construction of the liner for the sanitary landfill in the District of Visconde do Rio Branco, Minas Gerais (MG), Brazil. To evaluate the behavior of landfill leachate heavy metals through the soil, soil column tests were performed on samples of compacted soil. A computational program that uses an optimization procedure to generate values of the hydrodynamic dispersion coefficient and retardation factor was developed to facilitate interpretation of the results obtained by the cumulative mass method. Values of the retardation factor and hydrodynamic dispersion coefficient were not influenced by the method of determination, even when a reduced number of effluent samples was used by the cumulative mass method. The use of the cumulative mass method, based on a reduced number of pore volumes, reduces the time and cost involved in the tests.Key words: heavy metals, column test, cumulative mass method.
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 | 0.000 | 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".