Effects of partial cutting on the ectomycorrhizae of<i>Picea glauca</i>forests in northwestern Alberta
Bibliographic record
Abstract
This study assessed the impact of various harvesting practices (including those designed to emulate natural disturbances) on ectomycorrhizae (ECM) associated with white spruce (Picea glauca (Moench) Voss) in northwestern Alberta, Canada. Treatments included clearcuts, partial cuts (dispersed green-tree retention with 20%, 50%, and 75% residual live trees, and aggregated green-tree retention), unharvested control sites, and a burned stand. The percentage of active white spruce root tips and ECM richness and diversity, as observed in soil cores collected throughout the study site, all decreased with increasing disturbance intensity. Effects were particularly pronounced in clearcuts, machine corridors used for access by harvesting equipment in the dispersed green-tree retention stands, and in burned areas. Reductions in ECM biodiversity could be attributed to the sensitivity of late-stage ectomycorrhizae (e.g., Cortinarius spp., Lactarius spp., and Russula spp.) to soil disturbances and changes in microclimate associated with harvesting or burning. Areas of dispersed and aggregated green-tree retention were not dramatically different than unharvested forest in terms of root tip density and ECM richness, diversity, and composition. Harvesting practices that retain a percentage of residual live trees, either dispersed throughout the cutting unit or in aggregated patches, could be an effective means of maintaining ectomycorrhizal biodiversity at the stand level.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".