Do phosphorus amendments enhance biodegradation activity in stalled petroleum hydrocarbon‐contaminated soil?
Bibliographic record
Abstract
Abstract Phosphorus (P) fertilizers promote soil petroleum‐hydrocarbon (PHC) bioremediation by correcting carbon‐to‐P ratio imbalances. While these inputs create conditions favorable to microbial growth, areas of a site or an entire site with low degradation rates (i.e., “stalled”) occur for unknown reasons. We hypothesized that soil conditions limit P bioavailability, leading to stalls in PHC bioremediation, and adding the correct P amendment restarts microbial activity. Soils were collected and characterized from four cold calcareous PHC‐impacted sites in Saskatchewan, Canada, undergoing bioremediation. A generalized linear mixed model identified that regions with lower degradation rates possessed a neutral pH with high magnetic and salinity values. In a subsequent laboratory experiment, the proportion of benzene degraded at greater rates within active (i.e., higher degradation rates) than stalled soils, thereby following model predictions ( p ‐value = 0.19, Kruskal–Wallis). The PHC degradation efficiency of different P amendments was tested by doping stalled soils ( n = 3) with one of five treatments: 0 (control), 0 (autoclaved control), or 50 mg phosphate kg −1 soil as sodium diphosphate, triethyl phosphate, or tripolyphosphate. Tripolyphosphate accelerated benzene degradation (75.5 ± 5.4%) in one stalled soil (Outlook 323) and increased degradation non‐significantly (43.9 ± 9.4%) in another (Allan 917). Alternatively, the final sample (Davidson 421) possessed the greatest benzene removal with no amendments. This implies that soil P bioavailability may not be the sole cause of decreased microbial activity. Accordingly, combining model outputs with mineralogy and microbiology investigations could enhance PHC biodegradation rates in these cold calcareous soils.
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".