Bibliographic record
Abstract
Abstract. Pharmaceuticals and household chemicals are neither fully consumed nor fully metabolized when routinely used by humans, thereby resulting in the emission of residues down household drains and into wastewater collection systems. Since treatment systems cannot entirely remove these substances from wastewaters, the contaminants from many households connected to sewer systems are continually released into surface waters. Furthermore, diffuse contributions of wastewaters from populations that are not connected to treatment systems can directly (i.e., through surface runoff) or indirectly (i.e., through soils and groundwater) contribute to contaminant concentrations in rivers and lakes. The unplanned and unmonitored release of such contaminants can pose important risks to aquatic ecosystems and ultimately human health. In this work, the contaminant fate model HydroFATE is presented which is designed to estimate the surface-water concentrations of domestically used substances for virtually any river in the world. The emission of compounds is calculated based on per capita consumption rates and population density. A global database of wastewater treatment plants is used to separate the effluent pathways from populations into treated and untreated, and to incorporate the contaminant pathways into the river network. The transport in the river system is simulated while accounting for processes of environmental decay in streams and in lakes. To serve as a preliminary performance evaluation and proof of concept of the model, the antibiotic sulfamethoxazole (SMX) was chosen, due to its widespread use and the availability of input and validation data. The comparison of modelled concentrations against a compilation of reported SMX measurements in surface waters revealed reasonable results despite inherent model uncertainties. A total of 390,000 km of rivers were predicted to have SMX concentrations that exceed environmental risk thresholds. Given the high spatial resolution of predictions, HydroFATE is particularly useful as a screening tool to identify areas of potentially elevated contaminant exposure and to guide where local monitoring and mitigation strategies should be prioritized.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.030 | 0.011 |
| Insufficient payload (model declined to judge) | 0.380 | 0.301 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".