Algal DNA Repair Kinetics Support Culture-Based Enumeration for Validation of Ultraviolet Disinfection Ballast Water Treatment Systems
Bibliographic record
Abstract
To mitigate the potential spread of invasive species and pathogens, the International Maritime Organization and U.S. Coast Guard (USCG) adopted discharge performance standards for ballast water management that will take full effect in 2017. The USCG requires staining methods for enumerating ballast water treatment system (BWTS) efficacy. These stains do not detect DNA damage, the primary mechanism for ultraviolet (UV) disinfection, and neglect potential DNA repair after discharge. These factors necessitate investigation of enumeration methods for accurate validation and approval of UV-based BWTSs. To molecularly assess DNA damage and repair kinetics, UV-induced DNA lesions were quantified by an enzyme-linked immunosorbent assay in Tetraselmis suecica control and UV-treated samples that were cultured and tested over time. Most DNA repair occurred within 6 h, was essentially complete within 24 h, and was insensitive to light or nutrient conditions during incubation. Asymptotic repair kinetics indicated a maximum of 67% of DNA damage inflicted by 300 mJ/cm 2 was repairable. These data provide a novel UV dose response for DNA damage in T. suecica and indicate that enzymatic DNA repair kinetics are not affected by culture conditions. Because DNA is rapidly photorepaired, culture-based enumeration can be used to accurately validate UV BWTSs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".