Artificial network inference analysis reveals the impact of biostimulant on bacterial communities in fumigated soil for potato production against common scab
Bibliographic record
Abstract
Introduction and methods As part of a study on control methods of common scab disease impact on potato yield and quality, high-throughput sequencing was used to measure the effects of soil fumigant chloropicrin alone or in combination with a Bacillus species-based biostimulant on soil bacterial diversity in terms of richness and composition, as well as on soil bacterial network interactions. Results and discussion The results showed that common scab caused significant net yield losses of more than 46.25% in potatoes of control plots (T1), while the use of the fumigant alone (T3) and the use of the fumigant with the biostimulant (T4) reduced net yield losses to less than 2.5%. These treatments also promoted gross yield increases of 23.5 cwt. acre-1 (7.06%) and 28 cwt. acre-1 (8.41%) respectively. The study found that using the soil fumigant chloropicrin significantly and persistently altered the composition of the soil bacterial community over the growing season. The modifications of the soil bacterial community induced by the inoculation of the Bacillus species-based biostimulant are distinct by the end of the growing season depending on whether the soil has been fumigated (T4) or not (T2). Interestingly, artificial network inference analysis showed that the T2 treatment had the highest number of edges and linkages, contrary to the T3 treatment that had the lowest number of edges and linkages. The fumigation alone treatment leads to a reduction in interactions, while the application of the biostimulant, in both non-fumigated and fumigated soil, results in increased interactions and a higher number of connections within a phylum or between different taxa. Furthermore, the treatment combining the fumigant and the biostimulant exhibits a moderate increase in various network properties, providing evidence for the positive effect of biostimulant inoculation on bacterial communities in fumigated soils. Our results provide a more detailed understanding of the bacterial community structure and diversity in the soil of the different treatments. Moreover, deciphering network interactions in soil bacterial communities is fundamentally important for research in soil microbial ecology of potato cropping systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".