Interpreting collimated beam ultraviolet photolysis rate data in terms of electrical efficiency of treatment
Bibliographic record
Abstract
A novel approach is presented for using fluence-based rate constants from collimated beam ultraviolet (UV) degradation kinetics to estimate electrical efficiencies for large-scale treatment of chemical contaminants. Atrazine (ATZ) and N-nitrosodimethylamine (NDMA) are given as examples. The relative electrical efficiencies of medium-pressure (MP) and low-pressure (LP) mercury lamps for treating these contaminants estimated from collimated beam data compare favorably with data collected using a bench-scale annular reactor and two different waters. For the water with higher UV transmittance, ATZ degradation was more efficient with the MP lamp by 8%, while for the water with lower transmittance the LP lamp was more efficient by 4%. For NDMA, the LP lamp was more efficient than the MP lamp in both waters tested: it was 29% more efficient in the water with higher transmittance and 58% more efficient in the water with lower transmittance. Key words: chemical treatment, water treatment, ultraviolet radiation, pesticides, photochemical reactions, potable water, electric power demand.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".