Productivity of coastal Douglas-fir and western redcedar in response to species mixture, planting density, and soil carbon:nitrogen ratio
Bibliographic record
Abstract
Mixed-species plantations have been suggested as ecologically and economically viable alternatives to monocultures. We examined the growth response of coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) and western redcedar (Thuja plicata Donn ex D. Don in Lamb.) to different species mixtures (Douglas-fir : western redcedar mixtures of 1:0, 1:1, 1:3, and 0:1) and planting densities (500, 1000, and 1500 stems·ha−1) in a dry maritime forest on eastern Vancouver Island, Canada. Twenty-two years postplanting, species mixture significantly affected diameter and height growth (p < 0.001), with stand diameter and height generally decreasing with increasing redcedar composition. Inherent variation in soil productivity across the plantation (carbon:nitrogen ratio) equally constrained stand growth. The widest spacing had larger diameter than the closest spacing (p = 0.025) but the least stand basal area compared with the other spacing treatments (p = 0.003–0.031). Stand volume was significantly affected by mixture × density interaction (p = 0.024) and generally declined with increasing proportion of redcedar and decreasing stand density. In the first decades after plantation establishment, inherent species growth traits and soil fertility were most important in dictating stand productivity. Because of the differences in resource utilization of both species, stand dynamics may change as competition for light and soil resources increases. Evaluation of silvicultural recommendations regarding mixtures of both species will continue with plantation development.
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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) | 0.000 | 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".