i UNIVERSITY OF CALGARY Remote Estimation of Leaf Area Index in Forested Ecosystems
Bibliographic record
Abstract
ii Leaf area index (LAI) is defined as the ratio of the foliage area contained within a given area divided by the ground area in question. This quantity is a useful input parameter in various environmental modeling applications. Unfortunately, in-situ sampling of LAI is spatially limited and costly; therefore researchers have tried to relate remote sensing measurements to in-situ LAI measurements. Remote sensing models have traditionally attempted to use spectral vegetation indices to model variations in LAI. However, these models have achieved only moderate success because their accuracy is often dependent on the influence of the background. Recently, two promising techniques have been applied for remote LAI estimation: linear spectral mixture analysis and modification of spectral vegetation indices. These techniques offer explicit strategies for the mitigation of background effects. Additional remote estimation techniques have been developed specifically for this study, namely the scale factor and the normalized distance methods These remote estimation models are derived and compared for a region of montane forest in
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".