Predicting maximum branch diameter from crown dimensions, stand characteristics and tree species
Bibliographic record
Abstract
Forest resource inventories must include wood quality information to support the optimum use of wood fibre. The objective of this study was to develop models relating maximum live branch diameter (MBD), which affects lumber value, to tree and stand characteristics that can be measured through current and emerging remote sensing technologies. Using non-linear mixed effects models for six Canadian conifer species, as well as for three broad-leaved species, MBD was related to crown radius, tree height, crown length, stand basal area, and basal area of trees larger than the subject tree. Models that included only individual tree characteristics (crown radius, tree height, and crown length) did not perform as well as models that additionally included stand characteristics (stand basal area and basal area of larger trees). Models that took into account tree species performed better than models that did not; in particular, broadleaved species had much thicker branches than conifers. The best model did not show bias with respect to independent variables and had root mean square error of 0.32 cm. For the best model, prediction error was not related to silvicultural treatment. These model characteristics strongly support the potential to successfully predict MBD from remotely sensed data.
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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) | 0.003 | 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".