Investigation of microalgal tolerance and growth in simulated tailings pond water containing naphthenic acids
Bibliographic record
Abstract
Abstract Oil sands tailings ponds, such as those in northeastern Alberta, present environmental challenges due to slow sedimentation, water entrapment, and toxic naphthenic acids (NAs), which hinder water reclamation. This study assessed the tolerance and biodegradation potential of Chlorella vulgaris ( C. vulgaris ) and Parachlorella kessleri ( P. kessleri ) against three model NAs (Fluorene‐1‐carboxylic acid [F‐1], Trans‐4‐pentylcyclohexane carboxylic acid [T‐4], and Cyclohexane carboxylic acid [Cyclo]) under simulated tailings pond conditions. Microalgal growth was evaluated in Bold's Basal Medium at sodium chloride concentrations of 1.5, 2.2, and 4.5 g/L and NA concentrations of 40 and 130 mg/L, with biomass productivity, specific growth rate, and chemical oxygen demand (COD) measured. Both species tolerated salinity up to 4.5 g/L, though P. kessleri exhibited delayed growth at higher sodium chloride levels, while C. vulgaris maintained stable productivity. At 40 and 130 mg/L NA concentrations, C. vulgaris grew consistently across all NA types, with evidence of T‐4 biodegradation indicated by a 16% COD reduction. P. kessleri showed variable responses, with T‐4 enhancing biomass productivity by 70.3%, but it failed to grow heterotrophically on NAs without light. The increased NA concentration did not inhibit growth. C. vulgaris demonstrated greater adaptability and partial NA biodegradation potential, particularly for T‐4, while P. kessleri exhibited tolerance but limited degradation capacity. However, the persistence of natural NAs suggests that microalgae alone may not be sufficient for effective remediation, highlighting the need for integration with microbial consortia to enhance treatment efficiency.
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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) | 0.000 | 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) | 0.000 | 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 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".