Application of allometric systems for compatible area-based LiDAR-assisted estimation in the Province of Nova Scotia
Bibliographic record
Abstract
Light detection and ranging (LiDAR) is used to estimate tree, stand, and forest characteristics across large geographic areas. In the province of Nova Scotia, an enhanced forest inventory (EFI) was developed to provide high-resolution spatial forest inventory estimates across the landscape. For various forest attributes, independent LiDAR-based relationships were built leading to mathematical and biological inconsistency among forest attribute estimates. A systems approach, composed of allometric equations describing the relationships between volume per unit area, Lorey’s average height, basal area, quadratic mean diameter, and density, is developed to address these inconsistencies. Previous results showed that applying the systems approach provided reasonable and compatible estimates and eliminated inconsistency issues among forest attributes. This study evaluates application of the systems approach applied to eastern Nova Scotia using field data from a network of permanent sample plots and recent LiDAR acquisitions. The independent EFI estimates had inconsistencies of greater than 100% for basal area and implied stand-level form factor. These inconsistencies were eliminated using the systems approach. Results show that the systems approach can be scaled to larger landscape areas and that long-term field data can be leveraged to fit the allometric systems producing mathematically and biologically consistent estimates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".