Selection of Specific Endophytic Bacterial Genotypes by Plants in Response to Soil Contamination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plant-bacterial combinations can increase contaminant degradation in the rhizosphere, but the role played by indigenous root-associated bacteria during plant growth in contaminated soils is unclear. The purpose of this study was to determine if plants had the ability to selectively enhance the prevalence of endophytes containing pollutant catabolic genes in unrelated environments contaminated with different pollutants. At petroleum hydrocarbon contaminated sites, two genes encoding hydrocarbon degradation, alkane monooxygenase (alkB) and naphthalene dioxygenase (ndoB), were two and four times more prevalent in bacteria extracted from the root interior (endophytic) than from the bulk soil and sediment, respectively. In field sites contaminated with nitroaromatics, two genes encoding nitrotoluene degradation, 2-nitrotoluene reductase (ntdAa) and nitrotoluene monooxygenase (ntnM), were 7 to 14 times more prevalent in endophytic bacteria. The addition of petroleum to sediment doubled the prevalence of ndoB-positive endophytes in Scirpus pungens, indicating that the numbers of endophytes containing catabolic genotypes were dependent on the presence and concentration of contaminants. Similarly, the numbers of alkB- or ndoB-positive endophytes in Festuca arundinacea were correlated with the concentration of creosote in the soil but not with the numbers of alkB- or ndoB-positive bacteria in the bulk soil. Our results indicate that the enrichment of catabolic genotypes in the root interior is both plant and contaminant dependent.
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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 | 0.000 | 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 it