A practical assessment of nano-phosphate on soybean (Glycine max) growth and microbiome establishment
Bibliographic record
Abstract
) as a phosphate (Pi) fertilizer was evaluated as well as its impact on soil and soybean (Glycine max) bacterial and fungal communities. Microbial communities were evaluated in soy fertilized with nHA using ITS (internal transcribed spacer) and 16S rRNA high-throughput gene sequencing. Separate greenhouse growth experiments using agriculturally relevant nHA concentrations and application methods were used to assess plant growth and yield compared with no Pi (-P), soluble Pi (+P), and bulk HA controls. Overall, nHA treatments did not show significantly increased growth, biomass, total plant phosphorus concentrations, or yield compared with no Pi controls. Soil and rhizosphere community structures in controls and nHA treatment groups were similar, with minor shifts in the nHA-containing pots comparable to bulk HA controls at equal concentrations. The implementation of nHA in an agriculturally realistic manner and the resulting poor soy growth advises that contrary to some reports under specialized conditions, this nano-fertilizer may not be a viable alternative to traditional Pi fertilizers. If nano-phosphate fertilizers are to achieve their conjectured agricultural potential, alternative nHAs, with differing morphologies, physicochemical properties, and interactions with the soil matrix could be investigated using the evaluative procedures described.
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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".