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Record W2317155952 · doi:10.1061/41121(388)26

A Numerical Modelling Study for the Proposed Increase in Barramundi Production, Cone Bay, Western Australia

2010· article· en· W2317155952 on OpenAlexaboutno aff
Sasha Zigic, Oleg Makarynskyy, Scott Langtry, Guy Westbrook

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsBayEnvironmental scienceOceanographyFisherySeawaterHydrology (agriculture)EstuaryGeologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

Cone Bay is located in the Kimberly region of Western Australia. It is an area of fast flushing rates and dynamic circulatory patterns due to a 9 to 11 m tide, the second largest tide in the world after the Bay of Fundy in Canada. Marine Produce Australia (MPA) currently operates at a maximum production of 150 tonnes of arramundi per year within Cone Bay and was seeking a licence to increase the production into a commercialised venture of 1,000 tonnes per year. As part of the environmental assessment proposal, a staged desktop modelling study was carried out to examine the potential changes in circulation following the presence of sea cages and the potential for deposition and accumulation of fish waste and fish food. A side-by-side comparison of the circulation patterns indicated that for the existing environment currents within the operational area would be reduced to by the inclusion of the sea cages, but no changes in current directions were indicated. A numerical dye-study showed that Hushing times for the modified setting took approximately one day longer, due to retardation of the tidal flows. The fate of fish waste and food pellets was simulated at various sites of the existing operational area, and there was a tendency for the waste material to settle close to the release sites and along the east-west tidal axis. Based on these results, MPA had decided to position the sea cages a set distance from the boundaries to ensure that the potential effects are contained and that the environmental integrity of the area is maintained.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.182
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.307
Teacher spread0.260 · 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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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