Dataset for: The importance of incorporating landscape change for predictions of climate-induced plant phenological shifts
Bibliographic record
Abstract
Data taken from Chisholm et al. (2020): https://doi.org/10.3389/fpls.2020.00759 Article abstract: Warming in the high Arctic is occurring at the fastest rate on the planet, raising concerns over how this global change driver will influence plant community composition, the timing of vegetation phenological events, and the wildlife that rely on them. In this region, as much as 50% of near-surface permafrost is composed of thermally sensitive ground ice that when melted produces substantial changes in topography and microbiome conditions. We take advantage of natural variations in permafrost melt to conduct a space-for-time study on Ellesmere Island in northern Canada. We demonstrate that phenological timing can be delayed in thermokarst areas when compared to stable ground, and that this change is a function of both shifting species composition in these vegetation communities, as well as delayed timing within species. These findings suggest that a warming climate could result in an overall broadening of blooming and leafing windows at the landscape level when these delayed timings are taken into consideration with the projected advance of phenological timings in ice-poor areas. We emphasize that the impacts of geomorphic processes on key phenological drivers are essential for enhancing our understanding of community response to climate warming in the high Arctic, with implications for ecosystem functioning and trophic interactions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.032 |
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".