Phosphorus source driving the soil microbial interactions and improving sugarcane development
Bibliographic record
Abstract
Abstract The world demand for phosphate has gradually increased over the last decades, currently achieving alarming levels considering available rock reserves. The use of soil microorganisms, such as arbuscular mycorrhizal fungi (AMF), has been suggested as a promising alternative to improve phosphorus-use efficiency. However, the effect of the source of phosphorus on the interactions within the soil microbial community remains unclear. Here, we evaluated the links between the total dry matter content of sugarcane and the interactions within the soil microbial community under different phosphate sources, with/without AMF inoculation. The phosphate sources were Simple Superphosphate (SS, 18% of P 2 O 5 ), Catalão rock phosphate (CA, 2.93% of P 2 O 5 ) and Bayovar rock phosphate (BA, 14% of P 2 O 5 ). The results indicated that the BA source led to the largest total dry matter content. The phosphate source affected total dry matter and the structure of the soil microbial communities. The bacterial interactions increased across sources with high percentage of P 2 O 5 , while the fungal interactions decreased. The interactions between bacterial and fungal microorganisms allowed to identify the percentage of P 2 O 5 resulting in the highest total sugarcane dry matter. Our findings suggested the soil microbial interactions as a potential microbial indicator helping to improve the agricultural management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".