Growing trembling aspen and white spruce intimate mixtures: Early results (13—17 years) and future projections
Bibliographic record
Abstract
Controlled mixtures of trembling aspen (Populus tremuloides Michx.) and white spruce (Picea glauca [Moench] Voss) were established in 1989 at two locations in the Boreal White and Black Spruce (BWBS) biogeoclimatic zone in northeastern British Columbia. The initial study design of three aspen treatment densities of 0, 5000, and 10000 stems per hectare was expanded by reducing existing densities of aspen on a subset of plots to 1000 and 2000 stems per hectare. A random-coefficients regression model was used to analyze height and diameter growth trends for aspen and spruce 13–17 years after establishment. White spruce grown without aspen had significantly greater rates of height and diameter growth. There were no significant differences in spruce growth between the 5000 and 10000 aspen stems per hectare treatments. Differences in spruce height and diameter growth did not consistently display a pattern of declining growth as aspen density increased from 1000 to 10000 stems per hectare. Aspen responded to aspen density reduction by increased diameter growth of the remaining stems.The Mixedwood Growth Model was used to predict future growth of the experimental stands. Yield projections indicated that a total productivity gain of 21% may be achieved for mixtures compared to a pure spruce scenario. Over the range of conditions studied, spruce comprised approximately 40% of the total volume in mixed stands. These initial results will improve the assessments of the relative contributions that pure- and mixed-species management regimes may offer to achieving forest-level objectives.
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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 | 0.000 |
| 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 | 0.001 | 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".