Dynamic effects of soil mineral ion-microorganism interactions on phosphorus-sulfur cycling revealed by spatio-temporal data mining techniques
Bibliographic record
Abstract
Soil microorganisms are the main drivers in maintaining soil health.This paper focuses on the process of soil mineral ions and microorganisms involved in regulating the phosphorus-sulfur cycle, and systematically investigates the repair and improvement mechanism of soil microorganisms.Relying on an experimental area of a typical grassland in Inner Mongolia, we set up experiments with different nitrogen addition treatments, and combined with one-way analysis of variance (ANOVA) to investigate the distribution of soil phosphorus and sulfur fractions under various scenarios.Then, structural equation modeling was applied to explore the dynamic role between microbial action and phosphorus-sulfur cycle under N addition.Under different nitrogen addition scenarios, Ca10-P accounted for the largest proportion of inorganic phosphorus fractions, which were all greater than 40%.The percentage of inorganic sulfur in the soil was relatively small, less than 3% of total sulfur, and the response of inorganic and total sulfur to the gradient of nitrogen addition, nitrogen frequency, and different grassland management practices was not obvious.Fungal communities were important drivers of changes in functional genes for interleaf phosphorus and sulfur cycling at different N application levels, i.e., N fertilizer application altered the interleaf fungal communities by affecting soil physicochemical properties, which significantly regulated the interleaf bacterial communities, phosphorus and sulfur cycling functional gene abundance, and pathogenic fungal abundance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".