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Record W1968205652 · doi:10.1002/hyp.1035

Processes controlling the rapid drainage of two ice‐rich permafrost‐dammed lakes in NW Canada

2001· article· en· W1968205652 on OpenAlexafffundabout
Philip Marsh, Natasha Neumann

Bibliographic record

VenueHydrological Processes · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsEnvironment and Climate Change Canada
FundersAurora Research Institute
KeywordsPermafrostDrainageHydrology (agriculture)GeologyGlacierThermokarstGeomorphologyOceanographyGeotechnical engineeringEcology

Abstract

fetched live from OpenAlex

Abstract This paper considers the processes controlling the rapid drainage of ice‐rich permafrost‐dammed lakes. It is postulated that the primary process controlling lake drainage is the melting of ice‐rich permafrost, in a manner similar to that controlling the drainage of glacier‐dammed lakes. Two lakes are considered in the analysis: one that drained naturally over a period of less than 16 h, and Lake Illisarvik, which was experimentally drained in 1978. Preliminary analysis showed that the energy contained in the lake water was sufficient for melting the ice content of the resulting drainage channel for both study lakes. Discharge estimated using a glacier‐dammed lake model developed by Clarke ( Journal of Glaciology 1982; 28 : 3) compared reasonably well with measured discharge during the period of rapid channel enlargement at Illisarvik, and resulted in the draining of Trail Valley Creek lake within the brief period indicated by a gauging station. These results suggest that melting of the ice‐rich permafrost during drainage dominates at least the early stages of drainage. However, further work is required to consider the processes of mechanical erosion, which in some cases may dominate the later stages of drainage, and to consider the appropriateness of certain assumptions in the lake drainage model. Copyright © Environment Canada 2001 Reproduced with Permission of Environment Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.035
GPT teacher head0.241
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2001
Admission routes3
Has abstractyes

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