An Evaluation of Concepts and Tools for Managing the Northern Hardwoods
Bibliographic record
Abstract
Across the northern hardwoods of North America, there is considerable variation at the stand level (e.g. species composition, stocking, and growth) as well as among individual trees (e.g. stem quality, value, and growth). This variation challenges the management of the northern hardwoods by reducing the predictability and consistency of the biological and economic performance of both stands and individual trees. To overcome this variation, foresters have developed a wide range of tools, such as classification systems for evaluating individual trees and silvicultural systems for managing stands, which are based on concepts such as tree vigour, quality, and crown position. However, many of these tools and concepts have not been rigorously evaluated since they were first developed. Further complicating the use and continued development of such tools in the northern hardwoods is the lack of a common vocabulary for assessing the characteristics of individual trees. This thesis examines a selection of the tools used to manage the northern hardwoods, tests their efficacy, and provides recommendations for improvement. Specifically, it evaluates the vigour and quality classification systems as predictors of value, as well as the vigour and crown position classification systems as predictors of mortality and growth. It concludes that the existing systems can be simplified without sacrificing much accuracy. This thesis also establishes a structured comparison of four harvesting systems in order to evaluate their financial returns and silvicultural impacts, and proposes working definitions of the critical concepts that foresters use to characterize individual trees. The results indicate that some of the tools being used in the northern hardwoods can be improved and simplified with data that has recently become available. The results also suggest that tree selection priorities in the field should be adjusted. While this thesis is focused on sugar maple (Acer saccharum Marsh.) in central Ontario, its findings have broader theoretical implications on the development of silvicultural systems as well as classification systems in the northern hardwoods, which will be the subject of future work.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".