Simplified validation of the ELISA kit determination of Microcystins in surface water
Bibliographic record
Abstract
The enzyme-linked immunosorbent assay (ELISA), as a universal method for the determination of Microcystins, is of great significance for the rapid detection of Microcystins pollution. This study aimed to propose a simplified validation method for Microcystins ELISA kit by summarizing related documents and guidelines. After summarizing and clarifying from 20 validation parameters, 11 parameters were selected to simplify the validation of Microcystins ELISA kit. In addition, the acceptable range and validation details of each parameter were analyzed. The results indicated that the coefficient of determination of the Microcystin-LR standard curve was higher than 0.99. The concentration of quality control samples was within control limits. The accuracy of spiked and proficient samples was within 70%-130%. The variability of intra-assay, inter-assay, and reproducibility was less than 11, 15 and 21%, respectively. The LOD and LLOQ were 0.002 μg/L and 0.05 μg/L, respectively. When the concentration of Microcystins exceeded 5 μg/L, it was recommended to dilute the samples to the working range before detection. The specificity was estimated with seven Microcystin analogues and three amino acids, indicating that the cross-reactivity was less than 30%. These results revealed that the ELISA kit was satisfactory for detecting Microcystins in water.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".