How does environmental context influence the leaf phenology of tree species in Maritime Canada?
Bibliographic record
Abstract
Hemiboreal forest encompasses the shifting optimal distribution limits of both boreal and temperate forest types, providing an opportunity to develop insights for the potential effects of global change on each forest type. Leaf phenology, the timing of leaf life cycle events, serves as a dynamic indicator of biological response to climate change and signifies the potential robustness or susceptibility of particular species to future change. In order to better understand how environmental context influences the leaf phenology of hemiboreal tree species we installed a network of 34 leaf phenocam stations across Maritime Canada encompassing a range of 3° latitude and 2 °C in annual average temperatures. The most common broadleaf species observed were red maple (Acer rubrum) and paper birch (Betula papyrifera), while the most common needleleaf species we observed were red spruce (Picea rubens) and balsam fir (Abies balsamea). Our phenocam stations consist of a solar-powered consumer grade cellular time-lapse camera and colour reference panel, and were installed prior to and throughout the 2019, 2020, and 2021 growing seasons. We dissected image field of views into regions of interest corresponding to discernable individuals and used green chromatic coordinate curve fitting and threshold extraction approaches. We found that most species had a high degree of plasticity in phenological response to varying site conditions, though some had a conserved response to varying site conditions relative to other species. We also observed an unusually early fall green-down for paper birch at one site in July of 2021. This suggests that climate change may have differential effects on hemiboreal tree species due to phenology triggers being distinct among species. This work demonstrates the complexity of environmental influence upon leaf phenology, as well as the utility of phenocams in monitoring leaf phenology in remote regions of Maritime Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".