Examining 40-year stand development following vegetation control: white spruce planted in a trembling aspen dominated cutover
Bibliographic record
Abstract
Many of the major questions regarding stand establishment practices involve implications for longer-term ecosystem development. We examined 41-year treatment effects on stand composition and dynamics using a white spruce ( Picea glauca (Moench) Voss) planting, mechanical site preparation (MSP) – herbicide (2,4-D plus 2,4,5-T) trial in a trembling aspen ( Populus tremuloides Michx.) dominated mixedwood. Both barrel and blade MSP with planting increased total and white spruce year 41 stand-level biomass over that in untreated areas. Year 2 herbicide application reduced year 41 trembling aspen biomass without substantially increasing that of white spruce, resulting in total yields similar to those in untreated areas. Barrel MSP increased year 41 trembling aspen biomass over that of untreated areas whereas blade MSP reduced it. Herbicide-related declines in trembling aspen biomass persisted or increased with time whereas white spruce response to herbicide varied with time and MSP. By accounting for inherent growth patterns, age shift calculations gave more balanced temporal depictions of planted white spruce response than effect size or percentage gain calculations. With barrel MSP, stand composition demonstrated a degree of mixedwood homeostasis whereas with blading, trembling aspen composition declined unilaterally from year 20 to 41.
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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".