Applicability of remote sensing-based surface temperature regimes in determining deciduous phenology over boreal forest
Bibliographic record
Abstract
The study of deciduous phenology over boreal forest is important for understanding forest ecology and better management. In this paper, our objective was to determine the phenological stages of deciduous leaf out (DLO) over the deciduous-dominant [i.e. trembling aspen (Populus tremuloides)] stands in the Canadian Province of Alberta. During the period 2006–2008, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day surface temperature (TS) images to calculate accumulated growing degree days (AGDD: a favourable temperature regime for plant growth). The temporal dynamics of AGDD in conjunction with in situ DLO observations were then analysed in determining the optimal threshold for DLO in 2006 (i.e. 80 degree days). The implementation of the above-mentioned optimal threshold revealed reasonable agreements (i.e. on an average 91.9% of the DLO cases within ±2 periods or ±16 days of deviations during 2007–2008) in comparison to the in situ observed data. The developments could be useful in various forestry-related applications, e.g. plant growth and its ability of exchanging atmospheric carbon dioxide, forest ecohydrology, risk of insect infestation, forest fire and impact of climate change, among others.
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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 | 0.001 |
| 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 | 0.000 | 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".