Heterogenous Patterns in Leaf Phenology Across a Climate Gradient in Maritime Canada Observed through Phenocams
Bibliographic record
Abstract
Leaf phenology, the timing of leaf life cycle events, is a vital indicator of terrestrial biosphere function. The influence of global change upon leafing phenology in mid to high latitude regions is uncertain due to a complex interaction of drivers and lack of temporally and spatially resolved baseline data. Leaf phenology has been observed manually for millennia, and through satellite platforms for decades. A novel technique of monitoring leaf phenology known as near remote sensing employing time-lapse photography at the canopy level (or phenocams) allows for objective observations with high temporal and spatial resolution. We deployed 13 solar-powered time-lapse camera stations across a climate gradient in Nova Scotia, Canada to observe leaf phenology of locally abundant species including more than 300 individuals over the 2019 and 2020 growing seasons. To examine the influence of thermal, photoperiodic, and genetic drivers, our remote phenology monitoring stations were situated in comparative edaphic and topographic contexts and complemented with relative humidity and ambient temperature sensors. We observed variability in the timing of leaf budburst, peak of season greenness, redness, senescence, and abscission between and within species, despite similar degrees of environmental forcing. Moving forward, we will apply our insights to develop species specific process based models of leaf phenology, and test the wider application of our techniques to observational records from other regions. This work demonstrates the complexity of environmental influence upon leaf phenology, as well as the utility of phenocams in monitoring leafing 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".