Spatial patterns of microbial diversity and activity in an aged creosote-contaminated site
Bibliographic record
Abstract
Restoration of polluted sites via in situ bioremediation relies heavily on the indigenous microbes and their activities. Spatial heterogeneity of microbial populations, contaminants and soil chemical parameters on such sites is a major hurdle in optimizing and implementing an appropriate bioremediation regime. We performed a grid-based sampling of an aged creosote-contaminated site followed by geostatistical modelling to illustrate the spatial patterns of microbial diversity and activity and to relate these patterns to the distribution of pollutants. Spatial distribution of bacterial groups unveiled patterns of niche differentiation regulated by patchy distribution of pollutants and an east-to-west pH gradient at the studied site. Proteobacteria clearly dominated in the hot spots of creosote pollution, whereas the abundance of Actinobacteria, TM7 and Planctomycetes was considerably reduced from the hot spots. The pH preferences of proteobacterial groups dominating in pollution could be recognized by examining the order and family-level responses. Acidobacterial classes came across as generalists in hydrocarbon pollution whose spatial distribution seemed to be regulated solely by the pH gradient. Although the community evenness decreased in the heavily polluted zones, basal respiration and fluorescein diacetate hydrolysis rates were higher, indicating the adaptation of specific indigenous microbial populations to hydrocarbon pollution. Combining the information from the kriged maps of microbial and soil chemistry data provided a comprehensive understanding of the long-term impacts of creosote pollution on the subsurface microbial communities. This study also highlighted the prospect of interpreting taxa-specific spatial patterns and applying them as indicators or proxies for monitoring polluted sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".