Development of Height-Age Models for Estimating Juvenile Height of Coastal Douglas-Fir in British Columbia
Bibliographic record
Abstract
Abstract Douglas-fir (Pseudotsuga menziesii var. menzeisii) is an important and abundant tree species in coastal British Columbia. Juvenile height estimates are important for management prescriptions and decisions involving regenerating stands. We used 100 plots to investigate the juvenile height growth of coastal Douglas-fir. The growth patterns of the sample trees were observed by felling and splitting them longitudinally and measuring the height of the annual nodes from the point of germination. Sixty-four plots were used to develop a height model as a function of total age and site index. The Chapman-Richards, Gompertz, and modified exponential and power models were fit using nonlinear least squares regression. The models were tested with the remaining 36 plots. The modified exponential and power equation was the best fitting of the three models. None of the models met the regression assumption of independently normally distributed residuals with a mean of zero and a constant variance. The modified exponential and power model was further analyzed using the complete data set by fitting height growth and incorporating a model for serial correlation in the error term to improve the statistical properties of the model. West. J. Appl. For. 18(3):207–212.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".