Flowering time responses to climate differ between species in mesic and xeric habitats in Alberta
Bibliographic record
Abstract
Ongoing climate change is likely to put increased selection pressures on the phenology of plants, yet for many species their abilities to respond to environmental cues are unknown. The present research focuses on using herbarium specimens to examine how 14 native plant species in Alberta have adjusted or adapted to changes in temperature and precipitation over the past century. We specifically investigate the impact of flowering-time responses and determine (1) whether herbaria collections contain sufficient evidence of these phenological responses to climate in plant species in Alberta, and (2) whether the responses are dependent on the typical moisture regime of their habitat. We compared plants from mesic and xeric habitats in terms of their phenological responses to air temperature and precipitation. In this study, the taxonomic relationships between the species were considered by selecting 14 species representing seven different angiosperm orders (one pair of species for each order). By collating data on the peak flowering date over the past century using preserved specimens, we found that on average, species from xeric habitats are more responsive to temperature, but not precipitation. This tendency might be explained by the thermal properties of mesic habitats, a finding that may lead to ways to predict the degree to which environmental cues will govern flowering.
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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 | 0.000 | 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".