Taxonomic identification and diversity of effective soil microorganisms: towards a better understanding of this microbiome
Bibliographic record
Abstract
Soil microorganisms found in agricultural residues and the so-called efficient microorganisms (EM) are attractive for their potential applications and benefits in the bioremediation of complex ecosystems. However, the knowledge about Who is doing what?, as well as the trophic interaction in those communities that explain its benefits are limited; a better understanding of this microbiome is needed to explain its benefits. The objective of this research was to characterize the microorganisms isolated from two soil communities and the efficient microorganisms obtained in laboratory (EM16 consortium), taking into account physico-chemical characteristics, diversity, quantification, and taxonomic identification through microbiological and molecular techniques. A microbiological analysis was performed according to the morphological characteristics of the colonies as well as the study of the dynamics and taxonomic identification of the microbial populations through the TRFLP and Ion Torrent techniques. The diversity, dynamics, and taxonomic identification achieved in these studies showed the prospects for using these soil EM in bioremediation, considering the diverse metabolic pathways that these species have and their symbiotic interactive potential for biodegradation of lignocellulosic-resilient compounds. This study provides the first molecular characterization of the EM (EM16 consortium) and soil isolates from agricultural residues (sugarcane crop and bamboo field). The results suggest that the use of microbiological and molecular tools in a polyphasic approach allows the complete characterization of non-cultivable microorganisms that could contribute to sustainable environmental management and crop production.
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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) | 0.000 | 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 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".