HUMIC ACIDS ENHANCED REMOVAL OF AROMATIC HYDROCARBONS FROM CONTAMINATED AQUIFERS: DEVELOPING A SUSTAINABLE TECHNOLOGY
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
Contamination by gasoline and diesel fuels is a threat to groundwater resources. Polynuclear aromatic hydrocarbons (PAHs) which can represent up to 60% of volume in diesel fuels are of particular concern because many of them are carcinogenic and they are persistent, especially in oxygen-limited environment. Despite the development of alternative approaches, pump and treat continues to be the leading technology for the remediation of groundwater contaminated by gasoline and diesel fuels. The efficiency of this technology is however limited by the low solubility of the aromatic hydrocarbons. The objective of this study was to investigate the influence of humic acids on the removal of aromatic hydrocarbons from petroleum products in groundwater aquifers and to evaluate the potential use of humic acids, as a cost effective additive, in groundwater and soil remediation. In order to prove the feasibility of using humic acid in the field, a pilot scale experiment was conducted in a model aquifer with a very dense monitoring network, providing controlled conditions only possible in a semi-artificial system. In addition, different sources of humic acids were compared with surfactants for their ability to bind PAHs.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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