<i>Inonotus tomentosus</i> and the dynamics of unmanaged and partial-cut wet sub-boreal spruce–fir forests
Bibliographic record
Abstract
For wet sub-boreal spruce–fir forests (white spruce ( Picea glauca (Moench) Voss) × Engelmann spruce ( Picea engelmannii Parry ex Engelm.) – subalpine fir ( Abies lasiocarpa (Hook.) Nutt.)) in east-central British Columbia, we asked (i) do compositional and structural dynamics differ for unmanaged (UN) and partial-cut (PC) (50% removal 45 years before measurement) forests and (ii) how does Inonotus tomentosus Fr. (Teng) affect these dynamics? Inonotus tomentosus infected stands had 17% less spruce basal area (P = 0.059) than uninfected stands, but PC did not exacerbate I. tomentosus effects. PC and UN had similar live tree density, but UN had lower dead tree density. In all stands, snag longevity was typically <32 years, and ~40 years was required for dead wood to reach decay stage 3 or greater. UN was characterized by variable severity disturbances averaging ~8% of the canopy per decade. Management implications include the following: (i) harvest systems designed to emulate small-scale disturbance could remove trees at 8% of the canopy per decade, varied spatiotemporally, (ii) emulating dead wood abundance with partial cutting may be difficult given the impacts of partial cutting on dead wood abundance, and (iii) forests with moderate levels of I. tomentosus should not respond differently to harvesting than uninfected forests and thus require no special management.
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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) | 0.001 | 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".