Effects of species mixtures on growth and stand development of Douglas-fir and red alder
Bibliographic record
Abstract
In the Pacific Northwest, a mixture of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and red alder (Alnus rubra Bong.) often results when red alder regenerates naturally in planted conifer stands. The relationships among stand structure, tree mortality, tree size, and understory development in the two species mixtures were explored at two sites for the first 16 years after planting. Treatments included a range of species proportions, and red alder was either planted simultaneously with Douglas-fir or planting was delayed for 5 years. Red alder was also removed from some simultaneously planted proportions. Both replacement effects (total stand density held constant) and additive effects (stand density doubled) of the interaction were considered. Red alder grew relatively better at Cascade Head Experimental Forest in the Coast Range, while Douglas-fir grew better at H.J. Andrews Experimental Forest in the less temperate Cascade Mountains. Possible production benefits from mixed plantings were examined using two methods of calculation. Potential production benefits from certain planted proportions of the two species occurred at H.J. Andrews Experimental Forest. No planting time or species proportion resulted in yield improvements over monoculture stands at Cascade Head Experimental Forest. Understory species also varied because of differences in site and stand characteristics that resulted from the differences in planting times and species proportions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".