Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine
Bibliographic record
Abstract
Sustainable forest management requires accurate estimates of wood volume. At present, red pine (Pinus resinosa Sol. ex Aiton) is the most widely planted conifer tree species in southern Ontario, Canada. Therefore, inside and outside bark volume equations were developed for red pine trees grown in plantations. One hundred and fifty red pine trees were sampled from 30 even-aged plantations from across Ontario, Canada. Height-diameter pairs along the boles of sampled trees used to calculate stem volumes were obtained from stem analysis. Equations fitted to the data were a combined variable, modified combined variable, and modified form of dimensionally compatible volume equations. These equations were compared for their goodness-of-fit statistics, logical consistency, and predictive accuracy. The goodness-of-fit characteristics for all three equations were comparable for both inside and outside bark volumes. However, the estimated values for the intercept for the modified form of the dimensionally compatible and modified combined variable volume equations were negative and nonsignificant. The combined variable volume equation resulted in logically consistent parameter estimates in the presence of random effects parameters. Therefore, this equation was selected as the inside and outside bark volume equation for red pine trees grown in plantations. A nonlinear mixed-effects modeling approach was applied in fitting the final volume equation that included a weight (power function) to address heteroscedasticity. The equations developed here can be used to calculate inside and outside bark volumes of red pine plantations in boreal forests in Eastern Canada. These equations would require both diameter at breast height (DBH) and total height values in meters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".