Plant reproductive phenology along an elevation gradient in the extreme environment of the Canadian High Arctic
Bibliographic record
Abstract
Background The extreme environment of the Canadian High-Arctic is experiencing unprecedented climate change with temperatures rising at three times the global average. There is a compelling need to understand how the phenology of Arctic plants will respond. However, long-term High-Arctic phenology monitoring is challenging due to the region’s remoteness.Aim To predict phenological responses of Arctic plants to climate change using an elevation gradient with associated temperature gradient as a proxy for climate change.Methods Flowering and seed dispersal times of seven Arctic species were recorded along an elevation gradient on Ellesmere Island, Nunavut, Canada in 2015 and related to air temperature measured at plant height and growing day degree (GDD).Results Flowering and seed dispersal times were earliest at the warmest site. A significant relationship with temperature was observed in flowering times of five species and seed dispersal times of one species. Conspecifics experienced fewer GDD at peak flowering at the coldest site than at warmer sites.Conclusions Temperature gradient observations provide insights into phenology–temperature relationships that complement long-term monitoring and enhance our ability to understand the impacts of climate change in remote regions. However, potential species adaptation along the temperature gradient should be taken into account. This single summer of results should be viewed with caution.
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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 | 0.000 |
| 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".