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Freeze Bond Strength Development and Properties in Prolonged Submersion Times and Implications on Ice Rubble Strength

2024· article· en· W4404688963 on OpenAlexaffabout
M. T. Boroojerdi, Eleanor Bailey, Rocky Taylor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of NewfoundlandW.F. Baird & Associates Coastal Engineers (Canada)
Fundersnot available
KeywordsRubbleSubmersion (mathematics)Materials scienceGeotechnical engineeringGeologyMathematics

Abstract

fetched live from OpenAlex

This paper summarizes the finding of an extensive experimental campaign conducted at Memorial University of Newfoundland, focusing on understanding shear strength properties of freeze-bonded ice blocks. A series of Asymmetric Four-Point Bending (AFPB) tests were conducted, focusing on the effects of submersion time for two initial ice temperatures of -10 and -18 degrees, subject to a normal pressure of 25 kPa. Freeze bonds were then sheared at an actuator rate of 5 mm/ls. Freeze bond strength development can be separated into 5 stages, where bond strength increased with increase in submersion time in stage 1, reaching a peak after only 4 minutes of submersion, followed by a decrease in strength in stage 2, as ice reached equilibrium temperature with water. Freeze bond strength stayed at a constant low in stage 3. Freeze bond strength was seen to increase again in stage 4, eventually reaching the strength of solid ice in stage 5. This paper focuses on understanding the mechanisms that occur in stage 4 and 5 of freeze bond strength development. During these stages, freeze bond strength was observed to increase with increase in submersion time as submersion time increased from 24 hours to 2 weeks, and reaching the strength of solid ice after 1 week for ice with initial temperature of -10 degrees, and after 2 weeks for ice with initial temperature of -18 degrees.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.356

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 communication0.0000.000
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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2024
Admission routes2
Has abstractyes

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