Response of coastal Douglas-fir and competing vegetation to repeated and delayed weed control treatments during early plantation development
Bibliographic record
Abstract
A key silvicultural decision in managing young conifer plantations is determining the number and timing of release treatments to control competing vegetation. Three coastal Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plantations were treated under eight alternative herbicide regimes during the first 5 years after planting to test treatment effects on vegetation dynamics and seedling growth. After termination of herbicide treatments, competing vegetation developed at a rate similar to that of check plots, reaching 40%–60% cover in the first growing season and approaching 100% by the third. Recovery of competing vegetation was slightly more rapid with greater number of previous releases. Annual volume growth of seedlings was negatively correlated with current cover of competing vegetation, but competitive effects from previous years were fully accounted for by initial tree size. Under 4 years of release, delaying treatment by 1 year reduced volume attained at the end of 5 years by about 15%. Plots receiving 5 consecutive years of weed control reached the 5 year volume of check plots in only 3.9 years, implying an age shift of 1.1 years. Increasing the number of operational release treatments significantly improved seedling growth in the short term, but long-term growth effects must be monitored to determine the economically optimal regime.
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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 | 0.001 | 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 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".