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Record W326913034

Ice force measurements on the Pembina River, Alberta, Canada

2016· other· en· W326913034 on OpenAlexaboutno aff
F. D. Haynes, Donald E. Nevel, Dennis Farrell

Bibliographic record

VenueUS Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core) · 2016
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersCold Regions Research and Engineering Laboratory
KeywordsPierGeologySheet pileGeotechnical engineeringIce sheetBreakupPileGeomorphologyEngineeringStructural engineeringMechanics
DOInot available

Abstract

fetched live from OpenAlex

Abstract : Just before spring breakup in 1972, 23 in situ tests were conducted on the Pembina River, in Alberta, Canada, to measure ice forces. These tests simulated an ice sheet pushing against a bridge pier. The apparatus utilized a hydraulic ram to push a 5 1/2-in. (14.0-cm)-wide vertical pile section horizontally against the ice sheet, which varied from 11.5 to 19.5 in. (29.2 to 49.5 cm) in thickness. The velocity of the pile was varied from 0.07 to 21 in./sec (0.18 to 53.3 cm/sec) by hydraulic flow control valves. Both flat and round piles were used to represent the pier. Some tests began with the piles a few inches away from the ice sheet, whose edge was cut flat. Other tests began with the pile in contact with the ice sheet. For some of the round pile tests, augered holes were used to provide better initial contact. These in situ test results were compared with the ice force measurements made by other workers on a nearby bridge pier during ice breakup. The in situ test ice forces were about 50% higher than the bridge pier test results. This disagreement was caused by a difference between the sizes of the piles and the size of the pier and a three-day warming of the ice before the ice impacted against the pier. (Author)

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.255
Teacher spread0.208 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2016
Admission routes1
Has abstractyes

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