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Record W2120026230 · doi:10.1109/oceanse.2005.1513205

Environmental monitoring of fish plant effluent in coastal Newfoundland

2005· article· en· W2120026230 on OpenAlexafffundabout
Sara Adams, Neil Bose, Kelly Hawboldt, Tahir Husain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsMemorial University of Newfoundland
FundersNational Research Council CanadaFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities Agency
KeywordsPlumeEffluentEnvironmental scienceGeographic information systemHydrology (agriculture)Remote sensingEnvironmental engineeringGeologyMeteorologyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Offal from fish processing plants represents the largest organic waste stream in Newfoundland. It is important to understand the spatial dispersion properties of the waste to assess if there are possible options for waste management that may be advantageous to implement. Preliminary studies of a crab-processing plant's effluent were conducted in October 2004 whereby rhodamine WT, a common dye tracer was added to the effluent of a plant in Aquaforte, Newfoundland. Measurements were taken with a fluorometer, CTD (conductivity, temperature and depth) and dissolved oxygen sensor mounted on the back of an 18 ft inflatable boat equipped with an electric trolling motor to limit plume disturbance. Continuous spatial data corresponding to the sensor information was obtained using a GPS unit in the boat. Spatial and oceanographic data were integrated to obtain a visual and quantitative representation of the plume. A comparison of the actual plume was conducted with a plume dispersion model developed through the use of the plume modeling software, CORMIX and a geographic information system (GIS) to determine if there are any similarities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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.

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

Citations4
Published2005
Admission routes3
Has abstractyes

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