Effect of Corn Plant on Survival and Phenanthrene Degradation Capacity of<i>Pseudomonas</i>Sp. UG14Lr in Two Soils
Bibliographic record
Abstract
A study was undertaken to assess if corn (Zea mays L.) can enhance phenanthrene degradation in two soils inoculated with Pseudomonas sp. UG14Lr. Corn increased the number of UG14Lr cells in both soils, especially in the acidic soiL Phenanthrene was degraded to a greater extent in UG14Lr-inoculated or corn-planted soils than uninoculated and unplanted soils. The spiked phenanthrene was completely removed within 70 days in all the treatments in slightly alkaline soil. However, in acidic soil, complete phenanthrene removal was found only in the corn-planted treatments. The shoot and root lengths of corn grown in UG14Lr-inoculated soils were not different from those in non-inoculated soil between the treatments. The results showed that in unplanted soil, low pH adversely affected the survival and phenanthrene degradation ability of UG14Lr. Planting of corn significantly enhanced the survival of UG14Lr cells in both the bulk and rhizospheric soil, and this in turn significantly improved phenanthrene degradation in acidic soil. Re-inoculation of UG14Lr in the acidic soil increased the number of UG14Lr cells and enhanced phenanthrene degradation in unplanted soil. However, in corn-planted acidic soils, re-inoculation of UG14Lr did not further enhance the already active phenanthrene degradation occurring in both the bulk or rhizospheric soils.
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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 | 0.000 | 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) | 0.001 | 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 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".