Spatial analysis of a hydrocarbon waste‐remediating landfarm demonstrates influence of management practices on bacterial and fungal community structure
Bibliographic record
Abstract
Cultivation of dedicated soil plots called 'landfarms' is an effective technology for bioremediation of hydrocarbon waste generated by various industrial practices. To understand the influence of soil conditions on landfarm microbial communities, analysis of bacterial and fungal community structure using next-generation sequencing at different sections and depths was performed across a hydrocarbon-waste landfarm in Regina, Saskatchewan, Canada. While a core set of hydrocarbon-associated bacterial and fungal taxa are present throughout the landfarm, unique bacterial and fungal operational taxonomic units are differentially abundant at sections within the landfarm, which correlate with differences in soil physiochemical properties and management practices. Increased frequency of waste application resulted in strong positive correlations between bacterial community assemblages and elevated amounts of oil, grease and F3 - F4 hydrocarbon fractions. In areas of standing water and lower application of hydrocarbon, microbial community structure correlated with soil pH, trace nutrients and metals. Overall, diversity and structure of bacterial communities remain relatively stable across the landfarm, while in contrast, fungal community structure appears more responsive to soil oxygen conditions. Results are consistent with the hypothesis that years of bioremediation activity have shaped microbial communities; however, several management practices can be undertaken to increase efficiency of remediation, including the removal of standing water and soil tilling across the landfarm.
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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 | 0.001 | 0.001 |
| 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".