Scaling Approach for Estimating Stand Sapwood Area from Leaf Area Index in Five Boreal species
Bibliographic record
Abstract
This paper presents a scaling approach for estimating sapwood area at the stand level using knowledge obtained for individual trees of five boreal species: Populus tremuloides (Michx.), Pinus contorta (Doug. ex Loud. var. latifolia Engelm.), Pinus banksiana (Lamb.), Picea mariana (Mill.) BSP, and Picea glauca (Moench) Voss. Previously developed allometric models for sapwood depth and diameter at breast height for individual tree species were used to build stand level sapwood area estimates as well as stand level leaf area estimates, in pure and mixed vascular vegetation stands. A stand’s vegetation heterogeneity is considered in the scaling approach by proposing regression models for each species. The new combined scaling approach drew strong linear correlations at the stand scale between sapwood area and leaf area using observations taken in mixed stands of Southern Alberta, Canada. This last outcome suggests a good linear relationship between stand sapwood area and stand leaf area. The accuracy of the results was tested by observing each regression model’s adequacy and by estimating the error propagated through the whole scaling process.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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 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".