Additional file 1 of Large-diameter trees and deadwood correspond with belowground ectomycorrhizal fungal richness
Bibliographic record
Abstract
Additional file 1: Table S1. The basal area, number of stems, and range of ages for each of the woody species within the 1.15 ha forest sampled for fungal communities in Utah, USA. Table S2. The number of fungal sequences at each step of the DADA2 quality filtering pipeline. Table S3. The component loadings (Comp.1–Comp.5) of soil nutrients from a principal coordinate analysis at the Utah Forest Dynamics Plot, Utah, USA. Table S4. The proportional sequence abundance (%) and number of amplicon sequence variants (ASVs) for the fungal functional guilds within the Utah Forest Dynamics Plot, Utah, USA. Table S5. The Mantel correlation coefficients between distance and community composition of all fungi at the Utah Forest Dynamics Plot, Utah, USA. Bolded values are significant (p <0.05). Table S6. The Mantel correlation coefficients between distance and community composition of all ectomycorrhizal fungi at the Utah Forest Dynamics Plot, Utah, USA. Bolded values are significant (p <0.05). Table S7. The Mantel correlation coefficients between distance and community composition of all saprotrophic fungi at the Utah Forest Dynamics Plot, Utah, USA. Bolded values are significant (p <0.05). Table S8. The Mantel correlation coefficients between distance and community composition of tree biomass at the Utah Forest Dynamics Plot, Utah, USA. Bolded values are significant (p <0.05). Table S9. The Mantel correlation coefficients between distance and community composition of all deadwood biomass at the Utah Forest Dynamics Plot, Utah, USA. Bolded values are significant (p <0.05). Table S10. The rank of variables in the increase in mean square error, in Random Forest models for fungal diversity, by fungal guild, at the Utah Forest Dynamics Plot, UT, USA
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.875 | 0.228 |
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".