The Acadian Phenocam Network: Monitoring leaf and radial growth phenology to anticipate climate change impacts on forests
Bibliographic record
Abstract
Climate change is advancing leaf emergence in the spring and delaying leaf senescence in the fall. This extended leafing period may increase tree radial growth, with large potential impacts on wood supply and carbon sequestration, but empirical evidence supporting this remains limited. To address this, we have established the Acadian Phenocam Network (APN), a state-of-the-art monitoring system spanning 24 sites and 12 tree species in the Acadian forest in Nova Scotia, Canada. The APN integrates high-frequency observations of leaf phenology, radial growth, local meteorology and soil dynamics at each site. The APN will enable researchers to 1) quantify the response of leaf phenology and radial growth to seasonal weather regimes for a variety of tree species, 2) explore connections between leaf phenology and radial growth across a range of site and stand conditions, and 3) develop and enhance models to anticipate climate change impacts on phenology and growth. Further, the APN is designed to serve as a long-term observational system for continuously tracking climate impacts. Insights stemming from this network will support climate-focused forest management practices through characterizing the adaptive capacity of tree species and improved projections of forest growth and development.
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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 | 0.001 | 0.000 |
| 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".