Intralaboratory experience with a battery of bioassays: Colombia experience
Bibliographic record
Abstract
A joint effort to evaluate a battery of bioassays for regulatory purposes was conducted as an intercalibration exercise by institutions in eight countries (Argentina, Canada, Chile, Colombia, Costa Rica, India, Mexico, and Ukraine) with support from the International Development Research Centre (IDRC). The precision of the tests carried out in the Colombian Laboratory was evaluated by comparing the results obtained with the reference toxicants used as positive controls, as well as with a set of five blind samples which in fact had the same toxicant concentration (metolachlor plus cadmium). The coefficients of variation (CV) obtained with each bioassay for the positive controls ranged from 5 to 21% except for the Panagrellus test which gave CVs as high as 67%. The 100% sample concentration results of the mixture (metolachlor/cadmium) showed CVs between 2 and 55%. The highest value was again obtained with Panagrellus and the lowest value with the root elongation test. Even though the Panagrellus test had previously been used in our laboratory, its extra requirements in terms of time and training could be the reason for the high variability. The results also showed that the most sensitive test for heavy metals and pesticides was the Daphnia test. Hydra and Panagrellus tests showed the highest sensitivity response for the organics evaluated. © 2000 John Wiley & Sons, Inc. Environ Toxicol 15: 297–303, 2000
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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 | 0.036 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".