Influence of climatic conditions on growth rings of <i>Salix uva-ursi</i> Pursh from the southeastern shore of Hudson Bay, Subarctic Canada
Bibliographic record
Abstract
Over the past decades, warmer air temperature and spatiotemporal changes in the amount and patterns of precipitation have been observed at high latitudes. Such interannual variability in climatic conditions has a strong influence on the dynamics of biological processes regulating terrestrial ecosystems. Dendroclimatology can improve our understanding of the impacts of climate change on vegetation. Based on ring width and frost rings of bearberry willow (Salix uva-ursi) individuals sampled on the southeastern shore of Hudson Bay, Nunavik (Subarctic Québec, Canada), we built a reliable 105-year growth chronology. A complex dendroclimatological analysis, using the standard approach (mean temperature and precipitation sums), extreme variables, and various climatological indices based on daily data, was conducted in order to precisely describe the relationship between annual variations in growth rings and climatic conditions. The growth of Low Arctic willows in the Hudson Bay region is determined by changes in air temperatures as well as precipitation during the summer. However, temperature increases and reductions in amounts of rainfall may intensify drought stress, which is unfavorable for this species. We found that analysis of daily amounts and intensity of precipitation improved our understanding of the factors conditioning the growth of the studied species in an extreme habitat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".