Bioprospection of native psychrotolerant plant-growth-promoting rhizobacteria from Peruvian Andean Plateau soils associated with <i>Chenopodium quinoa</i>
Bibliographic record
Abstract
The Peruvian Andean Plateau, one of the main production areas of native varieties of Chenopodium quinoa, is exposed to abrupt decreases in environmental temperature, affecting crop production. Plant-growth-promoting rhizobacteria that tolerate low temperatures could be used as organic biofertilizers in this region. We aimed to bioprospect the native psychrotolerant bacteria of the quinoa rhizosphere in this region that show plant-growth-promoting traits. Fifty-one strains belonging to the quinoa rhizosphere were characterised; 73% of the total could grow at low temperatures (4, 6, and 15 °C), whose genetic diversity based on DNA amplification of interspersed repetitive elements (BOX) showed 12 different profiles. According to the 16S rRNA sequence, bacterial species belonging to the classes Beta- and Gammaproteobacteria were identified. Only three (6%) isolates identified as nonpathogenic bacteria exhibited plant-growth-promoting activities, like IAA production, phosphate solubilization, growth in a nitrogen-free medium, and ACC deaminase production at 6 and 15 °C. ILQ215 (Pseudomonas silesiensis) and JUQ307 (Pseudomonas plecoglossicida) strains showed significantly positive plant growth effects in aerial length (about 50%), radicular length (112% and 79%, respectively), and aerial and radicular mass (above 170% and 210%, respectively) of quinoa plants compared with the control without bacteria. These results indicate the potential of both psychrotolerant strains to be used as potential organic biofertilizers for quinoa in this region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".