Competitive Hierarchy Processes Support Mixed Species Regeneration in Strip-Cuts in the British Columbia Interior
Bibliographic record
Abstract
Competitive exclusion can lead to a loss of species diversity within small forest gaps. Thus, it poses a potential concern for mixed species regeneration following strip-cut harvesting in the species rich interior moist forests of British Columbia. In this study, we compare nine tree species widely used in reforestation for their growth rates along a light/gap-position gradient in 50 m wide east-west oriented strip-cuts. Data after 15 growing seasons reveal that a crossover of species’ growth patterns along the light gradient can be linked to their shade tolerance ranking, and competitive hierarchy processes can be observed between species. The growth of two shade-intolerant (lodgepole pine and ponderosa pine), and two moderately tolerant (Douglas fir and white pine) species did surpass the growth of other more tolerant species under high light conditions in this study. Thus, we conclude that maintaining shade-intolerant species within 50 m wide strip-cuts is feasible, but species need to be planted in locations that suit their light requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".