Interannual variations in snowpack in the Crown of the Continent Ecosystem
Bibliographic record
Abstract
Abstract Ecosystem changes such as glacier recession and alpine treeline advance have been documented over the previous 150 years in the Rocky Mountains of northern Montana and southern British Columbia and Alberta, a region known as the Crown of the Continent Ecosystem (CCE). Such changes are controlled, at least partially, by variations in snowpack. The CCE consists primarily of public lands, the majority of which is undeveloped or wilderness. Consequently, this region is well suited for an examination of long‐term snowpack variation and associated ecosystem change. Data from nine SNOTEL sites provide an indication of the daily accumulation and ablation of snowpack over the period 1977–2001, as well as the relationship between precipitation, temperature and snowpack. 1 April data from 21 snow courses indicated the extent of regional snowpack variation and trends over the period 1950–2001, and 1 May data from three snow courses in Glacier National Park allow this record to be extended back to 1922. SNOTEL data suggest CCE snowpacks are larger and more persistent than in most regions of the western USA, and that water year precipitation is the primary control on 1 April snow water equivalent (SWE). Snow course data indicate that variations in both 1 April and 1 May mean SWE are closely tied to the Pacific decadal oscillation, an El Niño–southern oscillation‐like interdecadal pattern of Pacific Ocean climate variability. Despite relatively stable snowpacks and summer temperatures since 1922, the glaciers in Glacier National Park have receded steadily during this period, implying a significant climatic shift between their Little Ice Age glacial maxima (ca 1860) and 1922. Published in 2002 by John Wiley & Sons Ltd.
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 | 0.001 |
| 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".