Bibliographic record
Abstract
Introduction Knowledge of plant-microorganism interactions is of great importance for bioremediation and phytoremediation. A wide variety of microbial populations live in natural and agricultural soils, and in marginal soils contaminated with xenobiotics. Plant roots strongly influence the surrounding environment, producing the so-called ‘rhizosphere effect’ in which microbial populations are qualitatively and quantitatively altered with, reciprocally, their metabolism directly affecting plant biology and the accompanying biota. Arbuscular mycorrhizal fungi (AMF) belong to the wide spectrum of soil microbiota and are able to improve the growth of the host plant, particularly in soils of low nutritional status or in those modified by human activity. This positive effect can be ascribed to the improvement of nutrient uptake by mycorrhizal colonized plant roots and the increase of soil volume explored for nutrient uptake by the plant, extending from areas in which nutrients have been exhausted to new regions where they are still available. An understanding of the interactions of arbuscular and vesicular-arbuscular mycorrhizas, together with the remaining soil microorganisms naturally associated with plant roots, will provide the basis for development of an important biotechnological tool for bioremediation. Bioremediation is a managed or spontaneous process in which biological, especially microbiological, catalysis acts on pollutant compounds, thereby reducing or eliminating environmental contamination (Madsen, 1991).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".