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Record W7095934064

Digital Video and Still Photographs Tides Preliminary Results Access to Data and Samples

2004· article· en· W7095934064 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDredgingSubmarine pipelineNatural (archaeology)Conceptual modelBaseline (sea)Natural resourceEnvironmental impact assessmentQuality (philosophy)Environmental monitoring
DOInot available

Abstract

fetched live from OpenAlex

Canada's ports and harbours require routine dredging to maintain operational viability and allow passage of deep-draft vessels. Dredge spoils from these operations are often placed in offshore disposal sites. Monitoring of these disposal sites is required to understand the long-term fate of the dredged materials. The Geological Survey of Canada (GSC), a division of Natural Resources Canada (NRCan) have initiated a project "Assessing Marine Environmental Quality in coastal Waters of Eastern Canada", that is designed to assess the effects of human activities in marine environments. The project will provide decision makers with geoscience information to resolve user conflicts and balance competing demands for seafloor use and development with conservation. One of the project priorities is to assess the impact of marine disposal of dredge spoil. Conceptual models will be developed for the behaviour of material in disposal sites under various marine environments, ranging from quite sheltered areas to exposed sites with high tidal and current stress. The project focuses on sites with differing degrees of human impact, management, and user conflict. The project will provide outputs, consisting of maps conforming to new marine-mapping protocols, databases in high-priority areas, conceptual models and reports. The primary

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.241
Threshold uncertainty score0.808

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.2410.075

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 head0.101
GPT teacher head0.285
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2004
Admission routes1
Has abstractyes

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