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Record W7132950905

Physical Processes in Ice-covered Lakes

2022· dissertation· W7132950905 on OpenAlexafffund
Bernard Yang

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoUniversity of Toronto ScarboroughVetenskapsrådetNational Science FoundationEesti TeadusagentuurMinistry of EnvironmentDivision of Ocean SciencesUniversities Space Research Association
KeywordsWater columnStratification (seeds)Thermal stratificationSurface waterThermalCurrent (fluid)Mixing (physics)
DOInot available

Abstract

fetched live from OpenAlex

Most lakes in the world are seasonally ice-covered, yet there is a wide discrepancy between our understandings of summer dynamics compared to dynamics underneath the ice. In this thesis, we investigate the important physical processes in seasonally ice-covered lakes and their connection with oxygen concentrations underneath the ice, a key indicator of ecological health. The main mechanism controlling the dynamics of ice-covered lakes is the nonlinear equation of state of fresh water, which has the important property that the density increases with temperature between 0oC and 4oC, and decreases for temperatures greater than 4oC. We use high frequency measurements of temperature, oxygen, electrical conductivity, and under-ice radiation in a large ice-covered lake to reveal that in early winter, the water column is inversely stratified where the temperature is increasing with depth. The thermal stratification strengthened at the bottom over winter, possibly due to gravity currents bringing relatively warm and salty water towards the bottom. In late winter when the ice cover melts, there is increased surface mixing driven by solar heating at the surface. At the same time, oxygen concentrations increased within the surface mixing layer, suggesting increased productivity by phytoplankton. To understand what physical processes influence the initial thermal stratification at the time of ice formation, we construct an analytical model that predicts the average temperature of the water column at ice-on based on the surface winds prior to ice formation, the lake depth, and the surface area of the lake. The prediction of this simple model agrees well with temperature data in 19 different lakes and suggests that large, windy lakes have lower temperatures at ice-on compared to small, calm lakes. Together, these results suggest that there are important processes in ice-covered lakes that could influence the physical and ecological conditions under the ice, and the winter conditions have important carry over effects into the open water seasons.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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 designSimulation or modeling
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
Published2022
Admission routes2
Has abstractyes

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