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Record W1587525810

Replacing The Gull Rock And Little Bay Islands Navigational Sites

2013· article· en· W1587525810 on OpenAlexaffabout
Stephen Wayne Lundrigan

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBayCoast guardGeologyTowerDamagesGuard (computer science)GeographyOceanographyArchaeologyEnvironmental protection
DOInot available

Abstract

fetched live from OpenAlex

The largest recorded hurricane to ever hit the province of Newfoundland and Labrador happened during the time span of September 20-21, 2010. After the destruction of the tropical cyclone was complete, the Canadian Coast Guard was responsible for assessing and fixing all damages caused to the provinces coastal navigational aids. While some sites were not damaged, others such as Gull Rock and Little Bay Islands had to undergo full structure replacement. Gull Rock and Little Bay Islands are both very low lying isolated islands that had their helicopter pads washed away, while Gull Rock lost its navigational tower as well. It is important that all Canadian waterways be safe and easily navigated to meet the standards that the Canadian Coast Guard strives for. It was necessary to design and implement stronger structures in the area. An analysis of waves in the area showed that the current structural designs were not braced properly in the horizontal direction. Larger timbers were chosen and braced in multiple directions for the helicopter pads, while the new navigational tower is a gravity-based design rather than a rock-anchored design. The following paper will outline the hurricane damage assessment, the design and replacement of both the navigational tower and helicopter pads, as well lessons learned from the project.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.176
Teacher spread0.171 · 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 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
Published2013
Admission routes2
Has abstractyes

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