Multi-Laboratory Study of Five Methods for the Determination of Brevetoxins in Shellfish Tissue Extracts
Bibliographic record
Abstract
A thirteen-laboratory comparative study tested the performance of four methods as alternatives to mouse bioassay for the determination of brevetoxins in shellfish. The methods were N2a neuroblastoma cell assay, two variations of the sodium channel receptor binding assay, competitive ELISA, and LC/MS. Three to five laboratories independently performed each method using centrally prepared spiked and naturally incurred test samples. Competitive ELISA and receptor binding (96-well format) compared most favorably with mouse bioassay. Between-laboratory relative standard deviations (RSDR) ranged from 10 to 20% for ELISA and 14 to 31% for receptor binding. Within-laboratory (RSDr) ranged from 6 to 15% for ELISA, and 5 to 31% for receptor binding. Cell assay was extremely sensitive but data variation rendered it unsuitable for statistical treatment. LC/MS performed as well as ELISA on spiked test samples but was inordinately affected by lack of toxin-metabolite standards, uniform instrumental parameters, or both, on incurred test samples. The ELISA and receptor binding assay are good alternatives to mouse bioassay for the determination of brevetoxins in shellfish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".