Differential temperature adaptation mechanisms in the High Arctic-adapted Regel’s chickweed ( <i>Cerastium regelii</i> Ostenf.) and the widespread longstalk starwort ( <i>Stellaria longipes</i> Goldie.)
Bibliographic record
Abstract
Climate change impacts Arctic ecosystems at rates four times the global average. Studying how species in these regions are responding will help conserve plant species at risk and informs understanding of current and future changes to Arctic landscapes. We explored herbaceous plant resiliency in this environment by asking how Arctic specialist Cerastium regelii Goldie (Regel’s chickweed) and generalist Stellaria longipes Ostenf. (longstalk starwort), a model of climate resilience, adapt to warming in High Arctic deserts after observing them across a natural temperature gradient in a permafrost disruption in Resolute (Qausuittuq), Nunavut, Canada. Stellaria longipes is a model of climate resilience, while little is known about the related C. regelii. In vitro warming studies showed S. longipes maximized growth at 24 °C with alteration of cytokinin metabolism, while C. regelii increased growth at 28 °C. These results highlight that Stellaria longipes is self-limiting at higher temperatures and less temperature-dependent for its success, while C. regelii is positively affected by warming temperatures. Our study increases understanding of plant resiliency in Canada’s High Arctic, representing the first description of hormone profiling and the second of environmental responses for understudied Arctic specialist C. regelii.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".