Comparison of approaches for estimating individual tree height–diameter relationships in the Acadian forest region
Bibliographic record
Abstract
Tree height can be a time-consuming measurement to obtain accurately in the field. Thus, height–diameter equations are frequently used to minimize costs associated with inventories and to reduce problems associated with height measurement errors. In this analysis, we compare three methods for estimating height from diameter data for a variety of species: (1) a nonlinear mixed-effects (NLME) model; (2) k nearest neighbour (KNN) imputation; and (3) a copula model. Predicted height values were compared with field height measurements for 922 trees across 24 species using paired point-wise and distribution-based goodness-of-fit criteria. All approaches performed very well, with the NLME and KNN imputation having better point-wise goodness-of-fit measures. Copula models, although generally poorer in terms of the paired point-wise goodness-of-fit, adequately predicted height values, maintained variances observed in field data, and showed the least loss of functionality when applied to species with sparse data or data with atypical parameters. Overall, the copula approach is flexible and may be more appropriate for estimating heights where paired point estimate accuracy is less important such as for tree lists that are subsequently used as inputs into growth and yield models.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".