Glucose and plant exudate enhanced enumeration of bacteria capable of degrading polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Enumerating environmental microbial isolates capable of polycyclic aromatic hydrocarbon (PAH) degradation can provide insight into the microbe-plant interactions that facilitate PAH removal. We examined a known PAH degrader ( Pseudomonas putida G7), a nondegrader ( Agrobacterium tumefaciens LBA4404), and several microorganisms isolated from the environment by using a PAH cocktail in an enumeration medium with or without 0.025% (m/v) glucose and (or) root exudates. Compared with the standard most probable number (MPN), the addition of glucose and root exudates in a modified MPN method resulted in a 3- to 11-fold enhancement of PAH degraders being enumerated among microorganisms found in PAH-contaminated soils. High-performance liquid chromatography analysis verified that PAH levels were reduced using this modified enumeration method. Low levels of glucose, perhaps in concert with other materials in exudates, may promote microbial metabolism, thereby enhancing PAH degradation.
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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) | 0.001 | 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".