Parameter estimation for modelling clogging of granular medium permeated with leachate
Bibliographic record
Abstract
A numerical model called BioClog is used to backcalculate biological activity rate constants using measured values of water quality and clog chemical characteristics from well-controlled laboratory column experiments that contained a granular-sized material permeated with synthetic and real leachates. BioClog is a multispecies, reactive chemical transport model capable of predicting clogging of a porous media caused by the accumulation of biofilms, chemical precipitates, and entrained particles. Monod kinetic constants for acetate- and butyrate-degrading bacteria were obtained through inverse modelling of granular-sized material permeated with synthetic leachate. The model predicted the changes in concentrations of volatile fatty acids and dissolved calcium and it predicted the changes in clog composition from a juvenile clog containing biofilm to a mature clog containing biofilm with mineral matter. The kinetic constants were then applied to predict spatial and temporal water quality and clog composition for a granular-sized material permeated with real leachate. The kinetic constants deduced through inverse modelling of the synthetic leachate column experiments provided reasonable predictions of the behaviour of the columns permeated with real leachate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".