The serpentine syndrome below ground: ectomycorrhizas and hypogeous fungi associated with conifers
Bibliographic record
Abstract
Serpentine soils select for unique plant communities, often with sparse vegetation. Mycorrhizal fungi mediate the interaction between plants and soils, yet little is known about the mycorrhizal fungi of serpentine-tolerant plants. Ectomycorrhizas and hypogeous fungal sporocarps were sampled on paired serpentine and nonserpentine soils in southwestern Oregon. We hypothesized that conifers on serpentine soils would have fewer species of mycorrhizal fungi, a distinct assemblage of ectomycorrhizal fungi, and fewer hypogeous sporocarps with less species richness. Sporocarps were sampled and soil cores collected around pines on serpentine and nonserpentine soils. Conifers on serpentine and nonserpentine soils hosted overlapping communities of ectomycorrhizal fungi, as characterized by nonmetric multidimensional scaling. From soil cores, we categorized 27 species by morphotype, of which 18 were identified by DNA. Fewer hypogeous sporocarps with less taxonomic richness were collected on serpentine soils. The lack of indicator species of mycorrhizal fungi and the greater variability among samples on serpentine soils suggest that soil composition does not determine the mycorrhizal community. The sparseness of host vegetation may limit the ability of fungi to grow from tree to tree and may increase the reliance on spore dispersal, thus creating a more varied pattern of mycorrhizal communities.
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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) | 0.002 | 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".