Tree-ring-based mass-balance estimates for the past 300 years at Peyto Glacier, Alberta, Canada
Bibliographic record
Abstract
Tree rings were used to reconstruct mass balance for Peyto Glacier in the Canadian Rocky Mountains from A.D. 1673 to 1994. Summer balance was reconstructed from tree-ring estimates of summer temperature and precipitation in the Canadian Rockies. Winter balance was derived from tree-ring data from sites bordering the Gulf of Alaska and in western British Columbia. The models for winter and summer balance each explain over 40% of the variance in the appropriate mass-balance series. Over the period 1966–1994 the correlation between the reconstructed and measured net balances is 0.71. Strong positive mass balances are reconstructed for 1695–1720 and 1810–1825, when higher winter precipitation coincided with reduced ablation. Periods of reconstructed positive mass balance precede construction of terminal moraines throughout the Canadian Rockies ca. 1700–1725 and 1825–1850. Positive mass balances in the period 1845–1880 also correspond to intervals of glacier readvance. Mass balances were generally negative between 1760 and 1805. From 1673 to 1883 the mean annual net balance was +70 mm water equivalent per year (w.e./yr.), but it averaged −317 mm w.e./yr from 1884 to 1994. This reconstructed mass balance history provides a continuous record of glacier change that appears regionally representative and consistent with moraine and other proxy climate records.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".