Bibliographic record
Abstract
In order to protect the environment that receives them, it is necessary to manage marine outfall discharges of wastewater effluents, which typically contain different types of contaminants even after treatment. The objective of this paper is to answer the question: ‘To what extent is the receiving environment being exposed to effluents?' The focus is on effluent spreading in the far field. A particle-tracking technique is presented, in which effluents are represented by a large number of particles and the particles' trajectories are tracked for given conditions of ambient flow and density stratification. The technique is particularly useful for examining the undesirable scenario of effluents rising to the water surface or coming into contact with the seabed. Advection, non-Fickian horizontal diffusion and Richardson number-dependent vertical diffusion are taken into account. The technique is successfully applied to the discharge of effluents into a tidal channel where the water column is density-stratified. In this application, predictions of the temporally and spatially varying effluent concentration field agree well with field data. A proper formulation of the effects of stratification on vertical mixing of the effluents in the ambient water is the key to success. The technique has shown advantages in handling large spatial gradients.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".