Inoculum Sources Modulate Mycorrhizal Inoculation Effect on Tamarix articulata Development and Its Associated Rhizosphere Microbiota
Bibliographic record
Abstract
(1) Background: Soil degradation is an increasingly important problem in many parts of the world, particularly in arid and semiarid areas. Arbuscular mycorrhizal fungi (AMF) isolated from arid soils are recognized to be better adapted to these edaphoclimatic conditions than exogenous ones. Nevertheless, little is known about the importance of AMF inoculum sources on Tamarix articulata development in natural saline soils. Therefore, the current study aims at investigating the efficiency of two AMF-mixed inoculums on T. articulata growth, with consideration of its rhizosphere microbiota. (2) Methods: indigenous inoculum made of strains originating from saline soils and a commercial one were used to inoculate T. articulata in four saline soils with different salinity levels under microcosm conditions with evaluation of rhizosphere microbial biomasses. (3) Results: Our findings showed that indigenous inoculum outperforms the commercial one by 80% for the mycorrhizal rate and 40% for plant biomasses, which are correlated with increasing shoot phosphorus content. Soil microbial biomasses increased significantly with indigenous mycorrhizal inoculum in the most saline soil with 46% for AMF, 25% for saprotrophic fungi and 15% for bacterial biomasses. (4) Conclusion: Present results open the way towards the preferential use of mycorrhizal inoculum, based on native AMF, to perform revegetation and to restore the saline soil microbiota.
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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) | 0.000 | 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 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".