Stand density management implications on the woody quality of yellow birch (Betula alleghaniensis Britton) in the Sault Ste. Marie MNR District of Ontario / by Richard Major
Bibliographic record
Abstract
"Creating quality tolerant hardwood stands through intensive silviculture and mapping their properties is considered a means for optimizing the value chain in Northeastern Ontario. A comprehensive literature review was conducted concerning the growth, morphology and factors influencing end merchantability of diffuse porous hardwoods, which commonly grow in Northeastern Canada and North America. It was seen that there was a gap in the literature concerning the effects varying degrees of density management have on the internal properties of the growing stock occurring on site. The literature did, however, provide a knowledge base from which to evolve. Based on the current gaps in the literature, mapping of the internal properties associated with density management of yellow birch was conducted from a research site 30 kilometers Northwest of Thessalon, Ontario in the Algoma Forest District. Density management associated with the specific research site reflect releasing trees to 10%, 20%, 30% and 40% of tree height at time of treatment, since the trees were on average 10m high the treatments consist of releasing plus trees to one, two, three and four m, respectively. Destructive testing was performed on 15 yellow birch (Betula alleghaniensis Britton) trees from the thinning trial located in the Northern regions of the Great Lakes St Lawrence forest zone. The results showed that the thinning treatments applied had a significant effect on the internal wood properties of the yellow birch growing on site. The greatest variability was not between treatments but axially throughout the trees. Janka Ball side hardness values attained from the test specimens were on average 24% higher than published values. Modulus of Elasticity (MOE) and Modulus of Rupture (MOR) values attained were 15% and 15% lower than the published values, respectively. The average ring width values across all treatments analysed were found to be 80% higher than the published values. The values for the microscopic attributes (fibers and vessels) displayed no difference between treatments and followed published trends associated with morphological changes in the trees. It was observed that the properties do not follow any discernable pattern associated with the intensity of crop tree thinning intensity. It was determined that thinning treatments do have a significant effect on the internal mechanical properties of the yellow birch growing on site and is suggested that thinning can increase stem merchantability and decrease rotation ages."-- from abstract.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".