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Record W6978333690 · doi:10.7939/r3-3j43-t421

Field and Laboratory Investigation of Frazil Floc and Surface Ice Properties

2024· dissertation· en· W6978333690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsnot available
Fundersnot available
KeywordsPancake iceSnowIce divideLead (geology)MeltwaterHydrology (agriculture)Arctic ice packIce formation

Abstract

fetched live from OpenAlex

Frazil ice particles and flocs can adhere to underwater structures causing blockage to water intakes and large accumulation of frazil ice in the channel may cause flooding and property damage. After frazil floc rises to the water surface the resulting surface ice profoundly impacts river hydraulics and bank stability. Significant progress has been made in investigating the properties and evolution of frazil ice particles. However, the physical process in which frazil particles flocculate into flocs and rise to the surface forming ice pans remains largely unknown due to limited data available on the properties of frazil floc and surface ice. The motivation of this study was to determine the properties of frazil floc and surface ice as well as their evolution under changing environmental conditions to better model and predict their physical behavior throughout the river freeze-up. The use of oblique images of river surfaces captured at long focus distances for long-term monitoring of surface ice conditions and ice pan properties was explored. Image data from a public camera mounted on a building rooftop captured during five freeze-up seasons was used. A deep learning based hybrid image processing algorithm was developed and evaluated to compute surface ice concentrations as well as ice pan sizes and shapes. The ice pans detected were generally elliptical shaped and their diameters ranged from 0.55 to 15.03 m. A lognormal distribution was a good fit for the ice pan size distributions for all years. Time series analysis showed that the appearance of ice pans coincided with supercooling and daily mean ice pan diameter varied from 1 to 3 m. These results demonstrate the viability of this method, which may open opportunities to identify and use public camera images for surface ice quantification. Properties of frazil flocs were measured for the first time in field by deploying a submersible camera system a total of eleven times during supercooling in the North Saskatchewan, Peace, and Kananaskis Rivers. A lognormal distribution was found to be a good fit for the floc size distribution. The mean floc size ranged from 1.19 to 5.64 mm and decreased linearly as the local Reynolds number increased. The average floc number concentration ranged from 1.80 × 10-4 to 1.15 × 10-1 cm-3. The average floc volumetric concentration ranged from 2.05 × 10-7 to 4.56 × 10-3 and was found to correlate strongly with the fractional height above the bed through a power law relationship. No significant correlations were found between the air-water heat flux and floc properties. Floc number concentration and mean size increased significantly just before peak supercooling and reached a maximum near the end of principal supercooling. To explore how the supercooling curve and frazil ice particle and floc properties vary under different air-water heat flux scenarios, a series of laboratory experiments were conducted in which frazil particles and flocs were generated and imaged when the cold room air temperature was increased or decreased threefold at different times during supercooling events. It was found that increasing the heat flux raised the mean particle number concentration by 25 – 33 % but did not significantly affect the mean floc number concentration. Decreasing the heat flux only produced significant effects when the change occurred before peak supercooling, reducing mean particle and floc number concentration by 10 and 22 %, respectively. Time series analysis showed that varying heat flux during different supercooling phases led to significantly different responses in the supercooling curve and particle and floc evolution. Additional laboratory frazil tank experiments were performed to investigate the correlation between the time series of frazil particle and floc properties under different air temperatures and turbulent dissipation rates. A strong linear relationship between particle and floc number concentrations was found with the floc-to-particle number concentration ratio ranging from 0.29 - 0.35. The ratio was reduced by 12 – 17 % when the turbulent dissipation rate was lower. A moderate to strong nonlinear correlation was found between mean particle and floc sizes described by an exponential relationship when particle mean sizes increased or decreased significantly. When particle mean size reaches an approximate equilibrium, a weak to moderate linear correlation was found between mean particle and floc size and the negative slope suggests they are inversely correlated.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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