Properties of soils influenced by ectomycorrhizal fungi in hybrid spruce [<i>Picea glauca</i> × <i>engelmannii</i> (Moench.) Voss]
Bibliographic record
Abstract
Soil properties of rhizosphere zones in coniferous forests are influenced by the presence of ectomycorrhizae. To elucidate the role of ectomycorrhizae (ECM) on the alteration of chemical and mineralogical properties of soils, soil pH, total C and N, cation exchange capacity, and the contents of mica, chlorite, and kaolinite, 2:1 type expandable clays, and amorphous minerals were compared in two soils, soils influenced by ectomycorrhizal fungi (ECS) and non-ectomycorrhizosphere soils (N-ECM) of Picea glauca x engelmannii (Moench.) Voss. Specifically, the two ECS soils were dominated by (1) Piloderma spp. (ECS-A) and (2) Inocybe lacera-like and Hebeloma-like morphotypes or where Piloderma spp. colonization was <1% (ECS-B). Our results showed that pH was lower in ECS compared to N-ECM samples. Total C and N were significantly higher in ECS soils than N-ECM samples. Cation exchange capacity as well as exchangeable K+, and Na+ were higher in ECS compared to N-ECM soils. X-ray diffraction analysis showed that the amount of 2:1 expanding clays (vermiculite and smectite) was higher in ECS than N-ECM samples and results suggest that there is an enhanced transformation of mica and chlorite to 2:1 type expandable clays in ECS samples when compared to N-ECM samples. The differences in chemical and mineralogical properties between ECS and N-ECM soils, in our study, support earlier studies that show ectomycorrhizal fungi can alter the properties of soils in the rhizosphere zone. Key words: ectomycorrhizosphere soils, soil properties, Piloderma spp.
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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 | 0.000 |
| 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".