Diversity of Soil Bacterial Community Is Influenced by Spatial Location and Time but Not Potato Cultivar
Bibliographic record
Abstract
Potato cultivars susceptible to common scab were previously reported to harbor five to six times more abundant pathogenic Streptomyces spp. in the rhizosphere soils compared with tolerant cultivars. It is still unclear if the diversity of soil bacterial communities is related to the abundance of pathogenic Streptomyces spp. This study evaluated the effects of potato cultivar on the diversity of bacterial communities in three spatial locations (soil located close to the plant [SCP], in the rhizosphere soil [RS], and in the geocaulosphere soil [GS]) in 2013 and 2014. Common scab tolerant (Goldrush and Hindenburg) and susceptible cultivars (Green Mountain and Agria) were planted in a field infested with pathogenic Streptomyces spp. causing common scab. The β-diversity of the bacterial community was significantly different between years and on dates within each year according to a permutational multivariate analysis of variance. The β-diversity also varied significantly among spatial locations (i.e., SCP, RS, and GS), probably due to changes in soil properties, but did not change significantly among potato cultivars. The architecture of the bacterial network in RS in 2014 was more complex compared with 2013 with a 2.5-fold increase in the number of bacteria included according to a co-occurrence analysis. These results indicated that the soil bacterial community diversity changed temporally and spatially. However, bacterial community diversity and richness were not affected by potato cultivar, suggesting that there were no relationships between bacterial community diversity or richness and the abundance of pathogenic Streptomyces spp.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".