Establishing a sustainable harvest for canada yew (<i>Taxus canadensis</i> marsh.) in Ontario
Bibliographic record
Abstract
In 2003, commercial harvest of Canada yew (Taxus canadensis Marsh.) in Ontario began—but without a sustainable harvest policy. In 2005, we began to determine the sustainability of three harvest intensity treatments at three sites in central Ontario. Harvest treatments were labelled control (no initial harvest), light (two-year-old shoots removed), moderate (three-year-old shoots removed), and severe (seven-year-old shoots removed). We also looked at effects of harvest season and light levels on shoot regrowth. After three and four years, severe-harvest plants yielded less than half the biomass of the initial harvest, while biomass from moderate-harvest plants was about equal to the initial. Biomass from light-harvest plants generally increased. Moderate light levels stimulated more first-year regrowth in all plants than low light levels did but increased only Year 2 regrowth in severe-harvest plants. Spring harvest reduced first-year regrowth only. Comparing biomass of moderate-harvest plants after three or four years with initial moderate-harvest biomass suggested similar growth rate across time periods. Our results concur with Canada Yew Association sustainable harvest guidelines: Moderately harvesting three-year-old shoots plus allowing four years of regrowth before reharvest ensures sustainable harvest, at least through one harvest cycle.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".