A Screening Level Mass Balance Model of Sediment Remediation Options
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is suggested that dynamic mass balance models can provide valuable support information when sediment remediation activities, such as dredging, are contemplated. A model with sufficient credibility and accuracy can be used to compare and contrast the results of various remediation actions and the effect of natural remediation processes. A food web model can also be included. The information provided by the model can be summarized in periodic (e.g. annual) “Report Cards” documenting the status of the contaminated system during and after the remediation process. Time plots of relevant properties of the system, such as contaminant masses, concentrations and levels in water, sediment and biota, can convey the essential information required for decision-making. This approach is illustrated by applying it with screening level accuracy to the remediation activities currently being undertaken in the St. Clair River, which flows from Lake Huron to Lake St. Clair near Sarnia, Ontario, Canada. The effects of dredging to reduce concentrations of hexachlorobenzene in sediment, water and biota are explored.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it