Study on Odor Detection and Microbial Identification Method in Closed Water Area of Jiangxi Province
Bibliographic record
Abstract
The problem of odor in water has become one of the most concerned issues of the public. The odor emitted from water will not only lead to deterioration of water quality, but also increase the cost of water treatment, which will have an adverse impact on the ecological environment. This paper takes the Doushui Lake water area as an example to study the odor detection and microbial identification methods by combining Sensory-GC with GC/MS, the research results show that: the water body of the Doushui Lake has a variety of odors, mainly grassy smell, musty smell, earthy smell, pungent smell, liquorice smell and burnt smell. There are four main substances, TCA, Geosmin, -Cyclocitral and -Cedrol, and their highest concentrations reach 124 ng/L, 97 ng/L, 43 ng/L and 48 ng/L, respectively. The decomposition of algae by microorganisms produces volatile gas compounds, mainly are dimethyl disulfide and dimethyl tetra sulfide. In the decomposition process, the contents of these two compounds both increase first and decrease later.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".