Bibliographic record
Abstract
The understory vegetation layer contributes considerably to the physical structure of boreal forests. This research sought to understand the relationships between field- and LiDAR- (light detection and ranging-) derived measures of boreal understory structure. As well as how environmental factors may influence discrepancies that can arise between these derived measures. Five attributes to map and characterize the boreal understory vegetation were selected: mean understory height, percent cover, density, complexity, and volume. Percent understory cover showed limited bias in LiDAR-derived estimates of compared to field measurements, in northeastern Alberta. However, LiDAR was shown to underestimate understory mean height and volume, and to overestimate understory density and complexity. Generalized linear model regression analysis were used to understand the influence of external environmental factors on these error patterns. Explanatory variables for these models included canopy openings, bole density, canopy complexity, and ecosite type. It was found that canopy openings reduced errors in understory mean height, percent cover, and volume. Higher bole density was strongly associated with increased errors in understory mean height and volume, and had weak influence on errors in understory percent cover, complexity, and density. More complex canopies were seen to slightly increase the errors in understory volume and did not influence errors in the remaining attributes. Finally, ecosite had a strong influence on errors in understory mean height, complexity, and volume. In the final phase of this research, a series of predictive maps of understory structure were developed across a 4300-hectare study area in the central mixed-wood subregion of the boreal forest, with independent-validation coefficients of determination ranging from 0.41 - 0.59.
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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 | 0.000 | 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 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".