Receptor Binding Assay for Paralytic Shellfish Poisoning Toxins: Optimization and Interlaboratory Comparison
Bibliographic record
Abstract
A receptor binding assay (RBA) for detection of paralytic shellfish poisoning (PSP) toxins was formatted for use in a high throughput detection system using microplate scintillation counting. The RBA technology was transferred from the National Ocean Service, which uses a Wallac TriLux 1450 MicroBeta microplate scintillation counter, to the California Department of Health Services, which uses a Packard TopCount scintillation counter. Due to differences in the detector arrangement between these 2 counters, markedly different counting efficiencies were exhibited, requiring optimization of the RBA protocol for the TopCount instrument. Precision, accuracy, and sensitivity [limit of detection = 0.2 microg saxitoxin (STX) equiv/100 g shellfish tissue] of the modified protocol were equivalent to those of the original protocol. The RBA robustness and adaptability were demonstrated by an interlaboratory study, in which STX concentrations in shellfish generated by the TopCount were consistent with MicroBeta-derived values. Comparison of STX reference standards obtained from the U.S. Food and Drug Administration and the National Research Council, Canada, showed no observable differences. This study confirms the RBA's value as a rapid, high throughput screen prior to testing by the conventional mouse bioassay (MBA) and its suitability for providing an early warning of increasing PSP toxicity when toxin levels are below the MBA limit of detection.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".