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Record W2949493427

Marine cargo transhipment solutions for mining companies

2019· article· en· W2949493427 on OpenAlexaboutno aff
Industrial Minerals

Bibliographic record

VenueIndustrial Minerals · 2019
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityLimitingDredgingEngineeringBusinessCapital costTransport engineeringOperations management
DOInot available

Abstract

fetched live from OpenAlex

Mining companies have been given several solutions to reduce transport costs, including no need for tugs, which reduces onshore infrastructure, and keeping vessel sizes in a range of 22,000-60,000 dwt that would mean project development is faster, easier and cheaper.The reduction of transport costs for the mineral sector through the use of high-capacity marine cargo-handling solutions was a key topic of this year’s annual Prospectors & Developers Association of Canada (PDAC) convention, held in Toronto in March. The newest generation of TSVs reduce the operational costs for miners by limiting capital expenditures on shoreside infrastructure, and on the maintenance dredging of harbors to accommodate deep-draft vessels. The firm emphasizes that it can support mining companies from the pre-feasibility study phase of projects throughout the life of a mine.In the initial stage, CSL will conceptualize solutions to transhipping for a customer, providing a detailed analysis of productivity, throughput, lead time, risk and cost.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.542

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 head0.097
GPT teacher head0.246
Teacher spread0.148 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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