Exploring the Canadian boreal forest using airsar, LandSAT5, and virtual lidar
Bibliographic record
Abstract
Data synergy or fusion is a mechanism whereby discrete types of data are used together to achieve a better understanding than was possible with each individually. Spanning over 30% of the Earth's landmass, the global forest plays a significant role in numerous planetary systems; the carbon cycle included. The objective of this paper is to couple simulated forest stands with measured datasets from various instruments to estimate a forest's mean canopy height and aboveground dry-biomass in regions where lidar measurements are sparse; we propose to create a virtual lidar instrument based on other readily available sensor measurements. We present a method to combine measured datasets with our sensor models to develop a classification algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height. We show the performance of our proposed method in regions lidar measurements as well as in regions lacking these measurements. Finally, we present our method using virtual lidar and show that there is minimal degradation in our estimation.
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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) | 0.001 | 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 | 0.001 |
| 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".