Spatial changes of soil solution and mineral composition in the rhizosphere of Norway‐spruce seedlings colonized by <i>Piloderma croceum</i>
Bibliographic record
Abstract
Abstract Ectomycorrhizae (ECM) or the root‐fungal association in forest ecosystems provide a unique soil microenvironment where soil properties and processes differ from the bulk soil. In this study, we would like to better understand the role of ECM systems in mineral weathering and its implications to soil formation and nutrient cycling in forest ecosystems. Specifically, we would like to document the spatial variations in the composition of soil solution and mineralogy of the rhizosphere as influenced by the ECM of Norway spruce + Piloderma croceum. Two‐month‐old seedlings of Norway spruce (control and colonized by P. croceum) were cultivated in special rhizotrons designed to allow spatial collection of soil solution. We used A and C horizons of a Dystric Cambisol collected from Höglwald forest near Munich. Micro suction cups (5 mm x 1mm) were installed in colonized and control rhizotrons, and soil solution was collected from September to November 2000. Our results show that the concentrations of NH$_4^+$ , Ca2+, and Mg2+ in the soil solution were lower in <1.0 cm than in >3.0 cm distance from the roots of Norway spruce, due to the possible range of influence of Piloderma mycelium reaching about 2–3 cm from the surface of the mycorrhizal root. In the rhizotron with soil from the A horizon, a higher phosphorus content in Piloderma‐colonized seedlings was observed. X‐ray diffraction data indicate that chlorite and possibly mica are being transformed to 2:1‐expanding clay minerals (probably smectite) within <1.0 cm distance from roots. The spatial variations in soil solution composition and mineral transformation are likely to be due to Piloderma colonization and concentrated mycelial growth within <1.0 cm distance from the roots. This is also evident in more intricate growth of mycelia on surfaces of micaceous minerals as compared to quartz. We assume that Piloderma modifies soil solution and mineralogy through acquisition of essential elements for its own survival and/or for the uptake by plant roots. However, the presence of spontaneous infection with wildtype ECM in the control plots may have altered the influence of Piloderma and must be taken into consideration when interpreting our results.
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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) | 0.000 | 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".