Rare rhizo-Actinomycetes: A new source of agroactive metabolites
Bibliographic record
Abstract
Numerous biotic and abiotic stress in some geographical regions predisposed their agricultural matrix to challenges threatening plant productivity, health, and quality. In curbing these threats, different customary agrarian principles have been created through research and development, ranging from chemical inputs and genetic modification of crops to the recently trending smart agricultural technology. But the peculiarities associated with these methods have made agriculturists rely on plant rhizospheric microbiome services, particularly bacteria. Several bacterial resources like Proteobacteria, Firmicutes, Acidobacteria, and Actinomycetes (Streptomycetes) are prominent as bioinoculants or the application of their by-products in alleviating biotic/abiotic stress have been extensively studied, with a dearth in the application of rare Actinomycetes metabolites. Rare Actinomycetes are known for their colossal genome, containing well-preserved genes coding for prolific secondary metabolites with many agroactive functionalities that can revolutionize the agricultural industry. Therefore, the imperativeness of this review to express the occurrence and distributions of rare Actinomycetes diversity, plant and soil-associated habitats, successional track in the rhizosphere under diverse stress, and their agroactive metabolite characteristics and functionalities that can remediate the challenges associated with agricultural productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".