ARTICLE Estimating stand-scale biomass, nutrient contents, and associated uncertainties for tree species of Canadian forests
Bibliographic record
Abstract
Abstract: The growing demand for bioenergy feedstock from forest harvest residues is generating concerns about the potential loss of site productivity through nutrient removal. We used tree-level national biomass equations and a national database of forest plots to develop stand-level biomass equations for the different tree components of 30 major forest tree species found in Canada using basal area as the independent variable. We have also compiledmore than 12 800 nutrient concentration values for different components of Canadian tree species from existing databases and the literature. Uncertainties were propagated across biomass equations and through conversion of nutrient concentrations to nutrient contents. Most of the uncertainty in nutrient contents estimates was found to be among the nutrient concentrationmeasurements. The greatest levels of uncertainty were for estimates of phosphorus in woody components and of calcium in foliage. Grouping species into genera gave only a minor loss of precision. The coupling of biomass equations and nutrient concentration data can be used to provide first-order estimates of biomass and nutrients exported by tree component and species when harvesting any commercial stand in Canada. However, the associated uncertainties are important enough to warrant their inclusion in decision making. Résumé: La demande croissante pour les résidus de coupe a ̀ des fins de bioénergie génère des préoccupations de perte de productivité suite a ̀ l'exportation d'éléments nutritifs. Nous avons développé des équations allométriques de biomasse a ̀ l'échelle du peuplement pour 30 espèces d'arbre dominantes de la forêt canadienne utilisant la surface terrière comme variable indépen-dante, a ̀ partir d'équations nationales de biomasse a ̀ l'échelle de l'arbre et de l'information provenant d'un inventaire national de
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".