Application of shadow fraction models for estimating attributes of northern boreal forests
Bibliographic record
Abstract
A shadow fraction method was developed previously for mapping forest attributes of northern black spruce ( Picea mariana (Mill.) Britton, Sterns & Poggenb.) forests. This paper evaluates application of the method for (i) balsam fir stands ( Abies balsamea (L.) Mill.), (ii) stands with higher volume and biomass than those of previous studies, and (iii) stands with a higher composition of deciduous trees and steeper slopes. Models developed for new test sites in (i) central Labrador and (ii) western Newfoundland were not statistically different from previous models for biomass, volume, and basal area. Relative root mean square errors (RMSEr) for central Labrador were slightly lower than those found in other test sites (RMSEr: 24%–29%) but higher for western Newfoundland (RMSEr = 37%–43%), attributed to the higher upper limit of measured attributes and increased presence of deciduous trees. Results suggest that reasonable estimates can be generated for conifer forests of northeastern Canada; however, an alternative solution is needed where mixed and deciduous stands are prevalent. Measurement of ground plots over a wider range of species composition and forest structure is recommended for broader application to northern boreal forests and to further assess the potential role of the shadow fraction method in national-scale inventory programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".