Characterization of natural organic matter and microorganisms within Fletchers Lake: A lake that discharges treated wastewater into a drinking water source
Bibliographic record
Abstract
Tools to evaluate natural organic matter (NOM) in drinking water are rapidly expanding. The purpose of this study is to characterize NOM within a small lake to determine the ability of characterization tools to detect differences in treated wastewater from background NOM. Fletchers Lake in Fall River, Nova Scotia is the study site for this work, which is the source water for the Collins Park Water Treatment Plant. The treated wastewater for the Collins Park area discharges into the same water source that is used for drinking water. Previous research from Dalhousie University has shown that membrane fouling in the plant is strongly related to the organic matter and microbial quality of the source; however, tools or signals to predict cleaning cycles are not available. In this study basic water chemistry analysis, such as water temperature, dissolved oxygen, total organic carbon (TOC), dissolved organic carbon (DOC), ultraviolet absorption at 254nm wavelength (UV254) and turbidity was performed. In addition, Fluorescence Excitation-Emission Matrix (FEEM) was used for NOM characterization. Implications of this study will assist Halifax Water in determining if municipal effluent is impacting the natural environment and determining which areas have high organic content that could affect efficiencies at the Collins Park Water Treatment Plant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".