Development of height to crown base models for thirteen tree species of the North American Acadian Region
Bibliographic record
Abstract
Height to live crown base (HCB) is an important input variable for several growth and yield models. Since HCB is rarely measured in the field, it is often predicted using static models. Instead of predicting HCB, the Forest Vegetation Simulator Northeastern Variant (FVS-NE) uses an equation that predicts crown ratio (CR), which has not been well validated. The main goal of the present study was to construct a regional HCB model for thirteen selected tree species of the Acadian Region of North America. The specific objectives were to: 1) evaluate FVS-NE model predictions, 2) compare suitable model forms, and 3) assess influence of various covariates to improve predictions. We evaluated three model forms, namely Holdaway (1986), logistic, and exponential. The findings indicated that FVS-NE models were significantly biased for all species as the overall mean bias and root mean square error (RMSE) were 0.11 m and 1.80 m, respectively. A logistic equation with size (diameter at breast height [DBH], total height [HT] and ratio of DBH to HT), and competition (crown competition factor [CCF] and basal area larger than subject tree [BAL]) gave the best predictions for all species in this analysis. This model had an overall mean bias <0.01 m and an RMSE of 1.59 m, which represents a significant improvement in predictions compared to FVS-NE. Despite the range of species and observed variation in the data, the equations worked well and can be easily calibrated to new stands with a few local observations.
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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) | 0.000 | 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".