‘Little Ice Age’ proxy glacier mass balance records reconstructed from tree rings in the Mt Waddington area, British Columbia Coast Mountains, Canada
Bibliographic record
Abstract
The intention of this research was to explore whether dendroclimatological relationships could be used to reconstruct long-term proxy records of ‘Little Ice Age’ glacier mass balance changes in the southern Coast Mountains of British Columbia. Tree-ring width chronologies from the Mt Waddington area were used in concert with historical glacier records to construct models spanning the past 450 years. The approach was to build models that were based on derived relationships between tree-ring growth and glacier mass balance: (1) warmer temperatures in the summer positively influence tree growth but are detrimental to glacier health; (2) colder temperatures during the winter and deeper snowpack have a negative impact on tree growth, whereas they are related to greater accumulation on the glacier during the winter season. Stepwise regression analyses were applied to tree-ring chronologies to predict glacier mass balance at local and regional scales. The models of net annual balance for the region (regional data set) show that periods of positive mass balance occurred in the AD 1750s, 1820s to 1830s and 1970s. Peaks of winter balance correspond closely to these periods, showing a sharp drop in winter mass balance towards the end of the nineteenth century. Wavelet analyses suggest that glacial mass balance regimes in the region respond synchronously to Pacific Ocean circulation systems such as the El Niño Southern Oscillation and the Pacific Decadal Oscillation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".