Bio-physicochemical effects of gamma irradiation treatment for naphthenic acids in oil sands fluid fine tailings
Bibliographic record
Abstract
Naphthenic acids (NAs) are persistent compounds that are components of most petroleum, including those found in the Athabasca oil sands. Their presence in freshly processed tailings is of significant environmental concern due to their toxicity to aquatic organisms. Gamma irradiation (GI) was used to reduce the toxicity and concentration of NAs in oil sands process water (OSPW) and fluid fine tailings (FFT). This investigation systematically studied the impact of GI on the biogeochemical development and progressive reduction of toxicity using laboratory incubations of fresh and aged tailings under anoxic and oxic conditions. GI reduced NA concentrations in OSPW by up to 97% in OSPW and in FFT by 85%. The GI-treated FFT exhibited increased rates of biogeochemical change, dependent on the age of the tailings source. Dissolved oxygen (DO) flux was enhanced in GI-treated FFT from fresh and aged source materials, whereas hydrogen sulfide (HS(-)) flux was stimulated only in the fresh FFT. Acute toxicity to Vibrio fischeri was immediately reduced following GI treatment of fresh OSPW. GI treatment followed by 4-week incubation reduced toxicity of aged OSPW to V. fischeri.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".