How do high phosphate concentrations affect soil microbial communities after a century of ecosystem self‐reclamation?
Bibliographic record
Abstract
The use of rock phosphate (RP) instead of soluble phosphate fertilizers is preferred for the development of more sustainable agriculture. However, the impact of high concentrations in RP on bacterial and fungal communities remains poorly documented. Thus, next-generation sequencing was used to characterize bacterial and fungal communities in the soils and roots of four plant species growing naturally in a self-restored ecosystem, on former open-pit phosphate mines where past exploitation generated locally a substantial phosphate enrichment of the soil. Our results show that bacterial communities are dominated by Actinobacteria and Proteobacteria phyla, while the Ascomycota and Basidiomycota phyla predominate in the fungal community. The alpha and beta diversities of both bacterial and fungal communities differ significantly between the root and soil compartments but are not significantly affected by RP inputs. However, Amplicon Sequence Variants (ASVs) indicative of RP-enriched soils have been identified; among them are bacteria representative of Streptomyces, Bacillus, Mycobacterium or Agromyces. Implications of these results open new ways of reflection to understand the microbial response following RP-inputs and long-term soil restoration, as well as to formulate microbial-based bioinoculants for sustainable agriculture applications based on microorganisms better adapted to high concentrations of RP.
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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 | 0.001 | 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".