Bibliographic record
Abstract
Oil refinery contaminated soils usually contain carcinogenic products, including polycyclic aromatic hydrocarbons (PAHs), and therefore require treatment for environmental and human protection. Biostimulation is the modification of external factors affecting the performance of indigenous bacteria and it may be employed to enhance natural attenuation at contaminated sites. Contaminated soil samples, provided by Imperial Oil (Sarnia, Ontario), were analyzed using pyrosequencing to sequence DNA strains in order to identify types of bacterial genus found in the samples. Flavobactericeae and Marinobacter were the two most dominant genus found in the sample, both recorded with the ability to degrade PAHs. \nFavourable conditions and governing parameters such as water holding capacity, moisture content, pH, total organic carbon, phosphorous, nitrogen, and potentially toxic environments, including sulfate and metal concentrations, were quantified in two experiments. The first experiment was batch studies containing a water: soil ratio of 10:1 (100 mL water: 10 g of contaminated soil sample). A second batch study, containing 50 g of contaminated sample with 60% saturation, was employed to determine effects of temperature. Plasma optical emission spectroscopy, Kjeldahl Method (for nitrogen), Olsen method (for phosphorous), gas chromatograph (GC), GC/MS, and HPLC were used for the analysis of samples. Urea was added to the sample as the nitrogen source and di-ammonium phosphate was added as the source of phosphorous. A target organic carbon: nitrogen: phosphorous (C:N:P) ratio of 100:10:1 was maintained for bacterial health. The pH was observed to be in the range of 6-8 throughout both experiments, and the water holding capacity of the samples was 22.7 wt%. Metal concentrations were found to be too low to cause toxic conditions for bacteria growth. Results suggest that the bacteria may have been using PAHs as a carbon source.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".