Total Phosphate Influences the Rate of Hydrocarbon Degradation but Phosphate Mineralogy Shapes Microbial Community Composition in Cold-Region Calcareous Soils
Bibliographic record
Abstract
Managing phosphorus bioaccessibility is critical for the bioremediation of hydrocarbons in calcareous soils. This paper explores how soil mineralogy interacts with a novel biostimulatory solution to both control phosphorus bioavailability and influence bioremediation. Two large bore infiltrators (1 m diameter) were installed at a PHC contaminated site and continuously supplied with a solution containing nutrients and an electron acceptor. Soils from eight contaminated sites were prepared and pretreated, analyzed pretrial, spiked with diesel, placed into nylon bags into the infiltrators, and removed after 3 months. From XAS, we learned that three principal phosphate phases had formed: adsorbed phosphate, brushite, and newberyite. All measures of biodegradation in the samples (in situ degradation estimates, mineralization assays, culturable bacteria, catabolic genes) varied depending upon the soil's phosphate speciation. Notably, adsorbed phosphate increased anaerobic phenanthrene degradation and bzdN catabolic gene prevalence. The dominant mineralogical constraints on community composition were the relative amounts of adsorbed phosphate, brushite, and newberyite. Overall, this study finds that total phosphate influences microbial community phenotypes whereas relative percentages of phosphate minerals influences microbial community genotype composition.
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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".