MétaCan
Menu
Back to cohort
Record W6891685612 · doi:10.4224/12328729

Friction of sea ice on various construction materials

2001· report· en· W6891685612 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2001
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaCanadian Wood Council
FundersMemorial University of Newfoundland
KeywordsSea iceCoefficient of frictionFriction coefficientHydraulicsStatic frictionLead (geology)Internal friction

Abstract

fetched live from OpenAlex

A series of tests was performed at the Canadian Hydraulics Centre to investigate friction between sea ice and various materials such as concrete, steel, wood and ice. The tests examined the effects of the change in the friction coefficient corresponding with the deterioration of material surface, speed, temperature, surface wetness and normal pressure. A carriage translated an ice specimen back and forth relative to samples of various construction materials fixed to the tank floor, while measuring the normal and tangential forces between the ice and the sample surface. Results from the test series indicated that friction was higher at lower speeds and also on rough materials. There was a great deal of variability observed in the instantaneous values of the coefficient of friction. Temperature had a weak effect on the friction coefficient, with slightly higher values of friction at higher temperatures, and there was a weak trend of lower friction with higher contact pressures. The average coefficient of friction of sea ice on smooth concrete, painted steel and sea ice was about 0.05 for speeds greater than 5 cm/s and increased to about 0.1 at 1 cm/s. The average coefficient of friction of sea ice on rough concrete and corroded steel was about 0.1 at speeds greater than 10 cm/s and increased to 0.2 at 1 cm/s.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.032
GPT teacher head0.289
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations10
Published2001
Admission routes4
Has abstractyes

Explore more

Same venueNPARCFrench-language works237,207