Erratum: Monitoring by laser-flow-cytometry of the polycyclic aromatic hydrocarbon-degrading <i>Sphingomonas</i> sp. strain 107 during biotreatment of a contaminated soil
Bibliographic record
Abstract
<br>A flow cytometric method (FCM) was used to detect and accurately enumerate a polycyclic aromatic hydrocarbon-degrading bacterial strain, Sphingomonas sp. 107, inoculated into a soil sample artificially contaminated with pyrene. To compare the FCM method with colony forming unit (CFU) assays, a rifampicin-resistant Sphingomonas sp. 107 was obtained which could be distinguished from the indigenous microflora, since there was no organism resistant to rifampicin in the soil that could transform indole to indigo (naphthalene dioxygenase activity). By combining light-scattering profiles (i.e., morphological properties), ethidium bromide influx (i.e., cell wall permeability), and fluorescence in situ hybridization against the 16S rRNA (i.e., detection specificity), we could enumerate the bacterial population of interest from the indigenous microflora and soil debris during the biotreatment. The FCM technique revealed that the number of inoculated Sphingomonas cells decreased gradually for 15 days of incubation before reaching a steady level of 7 to 12 × 105 cells·g-1 of soil. Similar values were obtained with the CFU assay. During this period, pyrene concentration decreased from 632 to 26 mg·kg-1 of dry soil. The FCM detection was improved by adding blocking reagent to the hybridization buffer to minimize the non-specific attachment of the fluorescent probe to soil particles. Combined with the improvements in probe technology, FCM detection was shown to be a good alternative to the conventional culture methods for the analysis of bacterial populations in environmental samples. This technique could be potentially useful for the detection of microorganisms that grow poorly in culture.Key words: polycyclic aromatic hydrocarbons, Sphingomonas, bioremediation, soil, laser flow cytometry.</br>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".