Tree growth at stand and individual scales in two dual-species mixture experiments in southern Washington State, USA
Bibliographic record
Abstract
Planting with mixtures of tree species rather than single species is often considered during reforestation because of the potential increased productivity and other benefits. We examined tree growth at the stand and individual tree scales in two experiments contrasting monocultures with a 1:1 mixture of tree species: (1) Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) with a conifer of similar shade tolerance (western white pine ( Pinus monticola Dougl. ex D. Don)) and (2) Douglas-fir with a more shade-tolerant conifer (western hemlock ( Tsuga heterophylla (Raf.) Sarg.)). There was no effect of mixture on growth or yield in the Douglas-fir – western white pine combination. In the Douglas-fir – western hemlock combination, yields in the mixture equaled those in Douglas-fir stands because of the enhanced performance of Douglas-fir in the mixture. For Douglas-fir, the height/diameter (h/d) ratio was significantly less in mixture, suggesting reduced competition for light when grown with western hemlock. In contrast, the h/d ratio for western hemlock was significantly greater in mixture, suggesting increased competition for light when grown with Douglas-fir. Neighborhood analyses showed that tree growth was directly related to initial size and inversely related to relative neighbor size and that the h/d ratio was positively related to relative neighbor size. In general, the size of a neighboring tree influenced growth more than species identity. Relationships between h/d ratios and growth rates suggest that growth differences between Douglas-fir and western hemlock in mixture will eventually increase.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".