Culture-independent analysis of Pseudomonas community structures in fertilized and unfertilized agricultural soils
Bibliographic record
Abstract
Pseudomonas community structures were investigated by analyzing 16S rRNA clone libraries derived from fertilized and unfertilized soil plots under corn–alfalfa rotation in a long-term experiment. Amplified 16S rRNA fragments derived by polymerase chain reaction (PCR) were cloned and sequenced. A total of 729 clone sequences were analyzed, of which 51 were possible chimeras and discarded. The remaining clone sequences (678) belonged to γ-proteobacteria with 61.8 % (419) classified to the genus Pseudomonas. Unclassified Gammaproteobacteria accounted for 23.4 % of total clones sequences. Rarefaction analyses showed a more diverse community structure of both Gammaproteobacteria and Pseudomonas in unfertilized than fertilized field soils irrespective of plant types under cultivation. Bacterial or Pseudomonas community structures differed significantly between fertilized and unfertilized soil plots. Clone sequences that are affiliated to Pseudomonas putida and P. oryzihabitans were more prominent in libraries from fertilized plots, while those that clustered with Pseudomonas frederiksbergensis were more often retrieved from unfertilized soil plots. A strong influence of fertilizer applications on community structure was supported by principal component analysis. We conclude that long-term use of mineral fertilizers could influence Pseudomonas community structure.
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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 | 0.001 |
| 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 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".