Predicting flowering phenology in a subarctic plant community
Bibliographic record
Abstract
Phenological studies are rarely reported from arctic and subarctic regions, but are essential to evaluate species’ response to climate change in these rapidly warming ecosystems. Here, we present a phylogenetic analysis of flowering phenology across an elevational gradient in the Canadian subarctic. We found that the timing of first flower was best explained by a combination of snowmelt, elevation, and growing degree-days. We also show that early flowering species have demonstrated lower intraspecific variability in their response to climate cues in comparison with late flowering species, such that individual flowering times of early species are more closely tied to environmental predictors. Previous work has suggested that early flowering species are more variable in their phenology. However, these studies have mostly examined variation in phenology over time, whereas we examined variation in phenology over space. We suggest that both patterns can be explained by the tighter coupling between phenology and climate cues for early flowering species. Thus, early flowering species have low intraspecific variance in flowering times within a single growing season as individuals respond more uniformly to a common set of cues in comparison with late flowering species. However, these same species may show large variance between years reflecting interannual variation in climate.
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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 | 0.000 | 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".