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Record W168141265

Sustainable dredging program on the lower Fraser River

2007· dissertation· en· W168141265 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSummit (Simon Fraser University) · 2007
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Sediment Control
Canadian institutionsnot available
FundersFisheries and Oceans CanadaTransport Canada
KeywordsDredgingEngineeringEnvironmental scienceCivil engineeringHydrology (agriculture)OceanographyGeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

The Fraser River Port Authority dredges the Fraser River to maintain navigation in support of the port activities. Sales of the dredged river sand are the only source of revenue offsetting the cost of dredging. The Port Authority does not have the key success factors to compete in the sand market. The burden of dredging impedes the economic development of the Fraser River Port. The Port Authority can change the status quo by extracting more value from the dredged river sand, implementing a user-pay system, reducing the scope of dredging, or obtaining government funding for dredging. The goals for the dredging program are efficient use of resources, equitable distribution of costs and benefits, no negative net impact on the environment, and acceptability to stakeholders. The analysis recommends that the Port Authority extract more value from the dredged material by utilizing it in land reclamation projects.

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.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.006
GPT teacher head0.203
Teacher spread0.198 · 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