Negative Binomial Regression Model for Road Accident Analysis in Hong Kong
Bibliographic record
Abstract
Excessive total nitrogen (TN) in the aqueous environment causes a notable negative impact on agriculture, human health, and the economy on a global scale. Conventional analytical techniques for determining TN in water usually involve long and tedious procedures with extensive sample preparation for digestion and titration. In recent years, lab-on-a-chip platforms have enabled in-situ measurements of water pollutants such as nitrate, nitrite, and ammonium. However, the digestion of organic nitrogen compounds in aqueous solutions still remains to be a challenge for portable analytical systems. In this paper, a portable TN analytical system is developed for on-site measurement of TN in a short assay time. It contains a TN reaction chamber for nitrogen digestion and reduction, and an optical measurement chamber for colorimetric determination of total nitrite. The ultraviolet-C (UVC)-thermal digestion method and the United States Environmental Protection Agency (EPA)-standard nitrate-nitrite determination method are implemented on the TN analytical system. Thorough investigations are performed to explore the optimum reaction conditions and reagent volumes in the process of TN oxidation, nitrate reduction, and nitrite detection, including oxidation time, temperature and substrate, oxidizer concentrations, nitrate reduction time, nitrite colorimetric reaction time, and reagents stability over time. Our system can complete fast oxidation and colorimetric determination of TN within 36 min, with a measurement range of 1 μg/L to 10 g/L and a limit of detection of 1.2 mg/L (lower than the World Health Organization standard of 10 mg/L). This portable TN analytical system enables the digestion and measurement of TN in a quick, portable, and low-cost manner.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".