Practical challenges when updating tree marking direction to prioritize crown dieback
Bibliographic record
Abstract
In Ontario’s Great Lakes – St. Lawrence Forest region, professional tree markers are often relied upon to select trees for harvest, based on the presence or absence of various defects. Conventional tree marking guidelines do not currently make any distinction between defects which affect tree vigour, and those that affect tree quality. It is desirable to retain trees that are high vigour, and therefore likely to continue to provide seed and ecosystem services to the residual stand for at least another 20 years, while harvesting enough high-quality timber to make these stand improvement cuts economically viable. For this reason, it is important that the distinction between vigour and quality be taken into consideration in tree marking prescriptions. Defects which have been shown to indicate that a tree is low vigour include crown dieback (CDBK), fungi, and cankers. Defects which indicate that a tree is low quality include fungi, cankers, cracks, cavities, and decay. CDBK is one of the most useful indicators that a tree is lower vigour when affecting at least 15% of the crown. This indicator could be given considerably more importance when tree marking. There is currently no official threshold used in Ontario, but CDBK will usually only be considered as a defect if it is affecting at least 50% of a tree’s canopy. The purpose of this study was to test out a proposed, simplified tree marking system which reflects these advances in our understanding of defects as indicators of vigour and quality and prioritizes the removal of low vigour/high quality trees (with ≥ 15% CDBK but no quality affecting defects). We compared this new prescription with two conventional prescriptions by having experienced tree markers use all three in eight different plots. We were expecting the new prescription to result in 1) improved residual stand vigour (because it prioritizes the removal of trees with CDBK) and 2) increased consistency between tree markers (because the defect list they were using was shorter than that of the conventional systems). Neither of these desired outcomes were achieved in practice – we observed no significant difference in CDBK marked or consistency between the three prescriptions. This study represents an important first try at updating tree marking direction. Subsequent trials will be necessary to refine the new prescription, to achieve desired management outcomes in the field.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".