Microbial activity and water-soluble trace element species in the rhizosphere of spring wheat (Triticum aestivum cv. USU-Perigee)
Bibliographic record
Abstract
The influence of microbial activity on the concentration and speciation of trace elements (TEs) was assessed in a study on the bioavailability of TEs for edible plants. A growth chamber experiment with spring wheat (Triticum aestivum cv. USU-Perigee) was conducted and the bulk (Bk) and the rhizosphere (Rz) soil components were collected at maturity. A characterization of the microbial activity and population was made by measuring the microbial biomass, enzymes (acid phosphatase, arylsulfatase, dehydrogenase and urease) and 16S rDNA DGGE profiles. In soil water extracts, major solutes (H(+), Ca, Mg, Na, NH(4), K, Cl, NO(3), SO(4), total N, DON and DOC) and trace elements (Al, As, Cd, Ce, Cr, Cu, Fe, Pb, Tl, and Zn) including monomeric Al species, free Cu(2+) and labile Zn were determined. The partition of the variation indicated that 12.1% of the distribution of TEs in the Bk soil was significantly and exclusively explained by chemical properties while this value was less than 0.1% for the Rz soil. To the contrary, microbial properties contributed significantly to 12.3% of the distribution of TEs in the Rz soil whereas it explained less than 0.1% for the Bk soil. Detailed redundancy analyses identified several potential mechanisms (e.g. weathering of primary mineral, solubilisation of sesquioxides, bacterial effect on the redox status) explaining the fate of TEs in the Bk and Rz soils. This study revealed that microbial activity is strongly associated to the speciation of trace elements in the Rz of edible plants and points to some microbial processes influencing TE speciation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".