The response of good and poor aspen clones to thinning
Bibliographic record
Abstract
The response of good and poor clones of trembling aspen (Populus tremuloides Michx) to thinning was assessed 16 years after treatment. Prior to the thinning treatment, the clones had been assessed as either poor or good using a rating matrix that considered height, diameter, quality and vigour of the clones. Results indicate that the 250 largest DBH stems∙ha −1 did not respond to thinning, irrespective of clone rating. The growth of these dominant trees was unaffected by smaller competitors. Considering all trees, the non-thinned (control) good clones were indistinguishable from the thinned good clones in terms of top height, basal area, quadratic mean DBH, volume∙ha −1 , and trees∙ha −1 16 years after treatment. For the good clones, 16 years of self-thinning yielded the same result as a single manual thinning. Due to a slower rate of self-thinning, the non-thinned poor clones retained some of the small stems longer and thus had a higher basal area and volume than the thinned poor clones. Thinning did not increase the piece size of the dominant trees so there was no associated increase in value.Thinning good and poor clones of trembling aspen did not increase the standing volume or piece size. Therefore, thinning is recommended only for good clones and only if it is profitable on its own. The literature on the benefits of thinning of aspen is contradictory. This may be due, in part, to undocumented clonal differences. Key words: trembling aspen, clones, thinning response, poplar, clonal rating
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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) | 0.000 | 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".