Overview of methods and results of the eight country International Development Research Centre (IDRC) WaterTox project
Bibliographic record
Abstract
Safe drinking water is essential for human health and sustainable development. In the last 30 years, the International Development Research Centre (IDRC) has funded over 200 applied research projects on tools and strategies for improving water and sanitation conditions in poor populations around the world. Realizing that the safety of drinking water is just as dependent on being toxicant free as on being pathogen free, the IDRC initiated WaterTox, a novel international research network with scientific institutions from Argentina, Canada, Chile, Colombia, Costa Rica, India, Mexico, and the Ukraine. The objectives of WaterTox were to develop and validate a battery of simple, inexpensive and practical bioassays for toxicity testing of water samples, to identify and validate appropriate sample concentration alternatives that would allow the battery of bioassays to assess the potential toxicity of waters used for human consumption, and to design, in collaboration with network partners, a strategy to promote the adoption of this battery for toxicity testing at the international level. The bioassays selected for WaterTox were based on the premise that they should be able to be performed in-country without the need for expensive imported supplies. A description of the procedures to establish the WaterTox project, the laboratories involved, the problems encountered, and results obtained are presented in this overview report. © 2000 John Wiley & Sons, Inc. Environ Toxicol 15: 264–276, 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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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".