Bibliographic record
Abstract
It is important to first understand the components and fate of an effluent plume in the receiving environment before its effects can be fully understood. The purpose of this work was to identify sensing technologies that can be used to monitor an effluent plume in the coastal environment and use selected sensors to compare the observed results with a predictive model. -- The three major coastal wastewater streams in Newfoundland and Labrador are seafood processing effluent, municipal wastewater and produced water. Each has various properties and components that should be considered when studying the plumes' potential effects. Once an outfall is established in a coastal environment, it is essential to continue monitoring to determine if there are any effects to the receiving environment. Both traditional and in situ sampling can be utilized in this task. Traditional sampling involves the use of bottles or divers to obtain samples at predefined coordinates. In situ sampling refers to the use of continuous sensors on a platform such as an autonomous underwater vehicle (AUV) to obtain measurements instantaneously (or the duration of sensor processing time) without the removal of the specimen or sample. In situ sensors are capable of measuring physical, chemical or biological parameters. -- Seafood processing waste represents the largest source of organic wastes in the province of Newfoundland and Labrador, yet is the least understood. Therefore, it was selected for further study. Trials were conducted at a seafood processing plant in Aquaforte, NL in October, 2004. Measurements were taken with a fluorometer (calibrated to detect rhodamine WT), a CTD sensor (used to measure conductivity, temperature and depth) and a 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 sensor information was obtained using a global positioning system (GPS) unit. Spatial and oceanographic data were integrated to obtain a visual and quantitative representation of the plume. -- Temperatures in Aquaforte Harbour were relatively constant with variations within one degree Celsius. Salinity was correlated with temperature and was shown to decrease towards the outer harbour region. Dissolved oxygen values were found to be supersaturated for all trials. Rhodamine WT (RWT) was found to follow a typical plume trend and was used for further plume analysis. -- A comparison of the R WT plume was conducted with the plume dispersion model, CORMIX. It was determined that this type of model can be used to simulate seafood processing wastes given that all the variables in the model are known. The trials were successful in demonstrating the use of this sensor package and dispersion model for monitoring a seafood processing effluent plume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".