Linking phenocam derived phenology with field observations in the boreal forest
Bibliographic record
Abstract
Bud phenology is a sensitive indicator of climate change. Therefore, it’s important to understand the direct relationship of canopy greenness and bud phenological events at higher spatial and temporal resolutions. Recently, phenocam based near surface remote sensing methods has been extensively used for time series analysis of canopy greenness to measure leaf and canopy phenological transition dates. Thus, this study compared the spring and autumnal bud phenological phases of black spruce[Picea mariana(Mill.) B.S.P] derived from field observations and greenness time-series data derived from phenocam. Results indicated that the 72th and 92th percentage of the GCC amplitude corresponded to the start and end of bud burst, respectively, and considered as the essential reference points for start of the canopy growing season. Whereas, end of bud set was represented by the 94th percentage of the GCC and may be considered as an option for the end of vegetation development. Overall, results enabled new dimensions to process time series phenocam-derived indices and to establish their relationship with field based observations.
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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 | 0.000 | 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 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".