Mise au point d'un tarif de cubage général pour les forêts québécoises : une approche pour mieux évaluer l'incertitude associée aux prévisions
Bibliographic record
Abstract
Merchantable volume assessment is of prime importance in forest management and for the estimation of wood production in Quebec Crown forests. Currently, this assessment is undertaken at the individual stem level according to a statistical model commonly identified as the Perron general stock table. This polynomial model is based on tree diameter at breast height and tree height. However, the mathematical model form and the method used to calibrate it do not enable a correct and detailed assessment of the uncertainty associated with volume assessments. This study describes a new model which, accounts for errors associated with the use of estimated height in volume forecasts and also limits the propogation of errors to sample plot and cruise line. Random effects have been specified in the model in order to take into account spatial correlation between observations made at the sample plot and cruise line level. Results indicate sample plot and cruise line random errors constitute components of model error, which individually range from 2 % to 4 % of volume assessment. Consequently, the basic premise that errors associated with volume assessment of individual stems are compensated by volume summations at the sample plot level is not valid. Key words: mixed model, random effect, error propagation, variance
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".