Production of regional single-entry volume tables and development of raw wood product mix model for Ontario
Bibliographic record
Abstract
Regional single-entry volume equations for northern \nOntario were derived based on the standard volume equations \nby Honer et al. A site stratification methodology was \nemployed to derive localized regional height-diameter \nequations. Using this method, the variation of height \nprediction for a given species within a region was greatly \nreduced. Thus, site specific equations were derived for each \nspecies. \nFor stand data lacking tree height information, the \nexponential function: Height = b1 x exp (b2/Dbh) proved best for \nheight prediction. This model was used to substitute height \nin standard volume equations. In addition to the total volume \nand gross merchantable volume based on top diameter and stump \nheight, the net merchantable volume based on age was also \nderived. \nStem profile equations were also fitted and used to \nmodel wood product mixes. The results showed that Max and \nBurkhart's model was the most accurate and precise model in \npredicting top diameters and section heights along the bole, \nwhile the model by Demaerschalk performed better for volume \nprediction. These stem profile equations demonstrated maximum \nflexibility in dealing with the wood product mixes. By \ncombining the stem profile models and the single-entry volume \nequations, a modelling system was developed to estimate wood \nproduct mixes for stands based on dbh distributions. The wood \nproduct mix model developed can be used at both the tree \nlevel and stand level. A Fortran program was written to \nfacilitate the calculations for modelling the combinations of \nwood product mixes at the stand level.
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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".