Reconstruction of Summer Temperature for a Canadian High Arctic Site from Retrospective Analysis of the Dwarf Shrub, Cassiope tetragona
Bibliographic record
Abstract
We used retrospective analysis of the widespread evergreen dwarf-shrub, Cassiope tetragona, to reconstruct average summer air temperature for Alexandra Fiord, Ellesmere Island, Canada. Retrospective analysis is a technique based on dendrochronological methods. In this study, chronologies are based on the morphological characteristics of the plant stems. Two growth and two reproduction chronologies, ranging from 80 to 118 years long, were developed from each of two populations at the High Arctic site. We used multiple regression models to develop a 100-year-long (1895–1994) reconstruction of July–September average air temperature that explained 45% of the climatic variance in the instrumental record. The reconstruction revealed an increase in summer temperature from ∼1905 to the early 1960s, a cooling trend from the mid-1960 to the 1970s, and an increase in temperature after 1980. These historical temperature patterns correspond well with those from other climate proxies from sites on Ellesmere and Devon Islands. As well, the similarity between our model and an arctic-wide proxy temperature time series suggests that the Cassiope-based reconstruction contains a large-scale temperature signal. There is great potential for the development of proxy climate data using Cassiope tetragona from sites throughout the Arctic.
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 | 0.001 |
| 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) | 0.000 | 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".