Jökulhlaups at Tulsequah Glacier, northwestern British Columbia, Canada
Bibliographic record
Abstract
Jokulhlaups from lakes dammed by Tulsequah Glacier in northwestern British Columbia have occurred regularly since the early twentieth century. The floods commenced after decades of downwasting and retreat of the glacier from its Holocene maximum position, which it achieved in the nineteenth century. Hydrometric data and other records are used to reconstruct the times and peak discharges of floods from two glacier-dammed lakes. Tulsequah Lake formed in the early 1900s when a tributary glacier separated from Tulsequah Glacier, creating an ice-free embayment between the two. The lake grew rapidly in surface area and volume in the first half of the twentieth century, but later, with continued glacier retreat, it decreased in size. The first jokulhlaups from Tulsequah Lake were the largest. Peak and total discharges decreased as the lake became smaller. Today, Tulsequah Lake is small, and it will disappear completely if Tulsequah Glacier retreats any further. A second lake (Lake No Lake), however, has formed and grown in size as Tulsequah Lake has diminished. Lake No Lake developed from a subglacial water body in a tributary valley, 7 km upglacier from Tulsequah Lake. Like Tulsequah Lake, Lake No Lake rapidly grew in area and volume during its youth, and in the 1 970s it began to generate its own jokulhlaups. Lake No Lake appears to be following the same evolutionary path as Tulsequah Lake-its volume is now decreasing due to downwasting of Tulsequah Glacier, and its jokulhlaups are beginning to diminish. As Tulsequah Glacier continues to shrink in response to climatic warming, additional glacier-dammed lakes may form, renewing the cycle of outburst flood activity. Such behaviour can be expected in other high mountains because most alpine glaciers are retreating in response to global warming.
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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 | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".