Optimization of the RTgill-W1 assay for commercial testing applications
Bibliographic record
Abstract
The RTgill-W1 in vitro assay is a new approach method designed as an alternative to one of the most widely used toxicity tests globally, the fish acute lethality test. The RTgill-W1 assay is standardized (Organisation for Economic Co-operation and Development; International Organisation of Standardization), but the test conditions could be optimized to allow for higher throughput, better replication, and lower costs. This study explores potential optimizations regarding the culturing conditions, plate format, and reference toxicant testing to make the RTgill-W1 assay more practical for widespread implementation. We demonstrate that the RTgill-W1 culture can be routinely split 1:3 without impacting test sensitivity (p = 0.207 to 0.612), which allows all work to be contained to a standard 5-day work week and 1.3× more tests over the current test methods. The test can adopt a 96-well plate format without impacting sensitivity (p = 0.672 to 0.889), dramatically improving the replication of the exposure wells and test controls and generating test data from a single plate. The fluorescent signal does not bleed across the smaller wells in the 96-well format to impact test endpoints (p = 0.465 to >0.999). The reference toxicity test concentrations can be modified to increase confidence in the point estimate (median effect concentration), allowing for more effective monitoring of assay performance. These optimizations improve the practicality and decrease the costs of the RTgill-W1 assay, which is particularly desirable for implementation in commercial and government laboratories that conduct regulatory toxicity testing.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".