The effect of mechanical site preparation on ectomycorrhizae of planted white spruce seedlings in conifer-dominated boreal mixedwood forest
Bibliographic record
Abstract
We characterized the ectomycorrhizae (ECM) of planted white spruce ( Picea glauca (Moench) Voss) seedlings as affected by mechanical site preparation (MSP) of clear-cut conifer-dominated boreal mixedwood forest. Relative abundance, richness, and composition of the ECM community were compared among untreated control, mixed, mounded, and scalped site preparation treatments. On >11 000 root tips, we observed 16 ECM morphotypes. Those common to the nursery in which the seedlings were raised were most abundant ( Thelephora americana , Wilcoxina -like (E-strain), Amphinema byssoides , Phialocephala -like (MRA)). Seedlings in the untreated controls had lower abundances of these, but higher abundances of other ECM, which were not present in the nursery of origin but were indigenous to these forest stands. In terms of ECM composition, the “mixed” treatment was most similar to the untreated control, while the “scalped” and “mound” treatments showed significantly different ECM communities than the controls. Our results suggest that MSP may facilitate continued dominance by ECM that establish on seedlings in the nursery while slowing the natural succession towards the natural forest ECM. MSP treatments that leave some surface organic matter relatively intact may impact ECM less than those that remove or bury the organic layer.
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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) | 0.001 | 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".