Remote sensing-based determination of understory grass greening stage over boreal forest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our objective was the determination of understory grass greening stage (GGS: defined as the date when 75% of the grass in the surrounding area of a particular location would be green) using remote sensing data over the boreal-dominant forested regions in the Canadian province of Alberta. We used moderate resolution imaging spectroradiometer (MODIS)-derived accumulated growing degree days (AGDD) and normalized difference water index (NDWI) with ground-based understory GGS observations at approximately 120 lookout tower sites during the period 2006 to 2008. During 2006, we extracted the temporal dynamics of AGDD/NDWI at the lookout tower sites and determined the best thresholds (i.e., 90 degree-days for AGDD and 0.45 for NDWI). These AGDD/NDWI thresholds were then implemented during 2007 and 2008; and observed that AGDD had better prediction capabilities in comparison to NDWI (i.e., ∼94% and ∼65% of the incidents fall within ±2 periods or ±16 days of deviations with the ground-based understory GGS observations using AGDD and NDWI thresholds, respectively). The outcomes would potentially be useful in understanding availability of food and habitat for wildlife species/animals; microclimatic environment, composition, and diversity of plant community; and forest fire danger and fire behavior in case of fire occurrences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it