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Record W4408428127 · doi:10.5194/egusphere-egu25-13090

Observations of Mixing and Deep Convection in a deep Fjord-Type Lake, Quesnel Lake, Canada

2025· preprint· en· W4408428127 on OpenAlexaffabout
S.A. Ibrahim, B. Laval, Svein Vagle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsFisheries and Oceans CanadaIBM (Canada)
Fundersnot available
KeywordsFjordMixing (physics)OceanographyGeologyDeep convectionClimatologyGeographyConvectionMeteorologyPhysics

Abstract

fetched live from OpenAlex

Deep lakes in temperate climates represent around 50% of the world’s surface, liquid freshwater storage, yet the mechanisms governing their seasonal deepwater renewal—and, in turn, their ability to support ecosystems—remain somewhat elusive. These lakes have depths of hundreds of meters thus experience extreme hydrostatic pressures, causing compressibility to significantly affect circulation. Combined with windstorms and inverse thermal stratification, this compressibility is hypothesized to trigger thermobaric instability which ultimately results in hypolimnetic ventilation. In this study, we investigate deep ventilation in Quesnel Lake, with a maximum depth of 511 m. The lake has a Y-shaped morphology formed by three arms and a horizontal extent of approximately 100 km. Our focus is on the East Arm, where the maximum depth occurs, which is surrounded by mountainous terrain which channels and amplifies wind forces.To assess long-term trends in deep ventilation, we analyzed data from two moorings within the East Arm (M9 and M14, respectively in 500 and 400m water depth), including years when they were deployed independently or when meteorological stations were inactive. To better understand deep water renewal mechanisms that occur during individual events, we focused on two winters with the most comprehensive coverage of water temperature and meteorological data. In 2007, M9 and M14 were operational simultaneously, complemented by a third mooring (M11 in 175m of water) and a meteorological station both near the eastern end of the East Arm. In 2023, M14 and M9, as well as a weather station at Hurricane Point (the narrowest section of the East Arm) were all simultaneously operational.For M9 (2003–2012, 2024) and M14 (2007, 2016–2024), a significant series of events during inverse-thermal stratification occurred in each observational year in mid-January. These events were observed to consistently reset the bottom temperature, evident as a rapid cooling as expected from thermobaric instability. We observed two distinct cooling modes. The first is characterized by the sequential vertical descent of cool water plumes through each mooring from top to bottom, which is typically associated with thermobaric instability theory. The second mode involves a sudden horizontal intrusion of colder water at depths of 400 m and 500 m, while the shallower thermistors are less affected. In January 2007, these series of events led to a net cooling of around 0.25°C at the deepest point of the lake (M9) and 0.4°C at M14. In both 2007 and 2024, meteorological data showed that windstorms, necessary to trigger thermobaric instability, accompanied by severe sub-zero air temperatures (reaching -23°C) preceded the bottom water-cooling events. Whether the mechanism of deep-water renewal occurs vertically or horizontally, over two decades of records consistently reveal an interaction between the lake’s deepest regions and surface waters.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.219
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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