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Record W2038509973 · doi:10.1061/41098(368)29

Port of Ehoala—Design of Navigation Works

2010· article· en· W2038509973 on OpenAlexaff
Brent T. Sumner, Trevor Elliott, William F. Baird, David J. Werren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsW.F. Baird & Associates Coastal Engineers (Canada)
Fundersnot available
KeywordsDredgingPort (circuit theory)Government (linguistics)Public–private partnershipBreakwaterEngineeringTailingsGeneral partnershipCivil engineeringBusinessGeologyOceanographyFinance

Abstract

fetched live from OpenAlex

The island country of Madagascar is rich in natural resources such as the mineral sand ilmenite (titanium dioxide), which is used as a pigment in the production of paints, coatings and plastics. International mining group Rio Tinto Plc is now mining ilmenite deposits located near the town of Fort Dauphin (Tôlanaro) in the southeastern part of the country for export to processing facilities in North America. Existing infrastructure in the region was severely limited and a new port was needed, one capable of initially exporting a minimum of 750,000 metric tonnes/yr (827,000 ton/yr). The construction of the new port is a public/private partnership between Rio Tinto and the World Bank and forms part of a total 1 billion USD development with major stakeholders including the Government of Madagascar, World Bank, and Rio Tinto. W.F. Baird and Associates was the Engineer for the greenfield port project responsible for the planning, design, and construction management of the new 145 million USD Port of Ehoala. The new port is comprised of five main elements: breakwater, quay, dredging and reclamation, navigation works, and other coastal structures. This paper will present challenges and solutions related to the navigation works, which include the access channel, turning basin, maneuvering areas around the quay, and the aids to navigation.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.748

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)0.0010.000

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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
Published2010
Admission routes1
Has abstractyes

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