Metals in the Human Environment Strategic Network (MITHE-SN): The Interface of Risk Assessment, Public Policy, and Advocacy
Bibliographic record
Abstract
A 5-year strategic research network with a diverse base of industry, government, and academic partners was approved for support by National Sciences and Engineering Research Council of Canada (NSERC) on January 3, 2005. This Metals in the Human Environment Strategic Network (MITHE-SN) builds on, and further extends, science knowledge developed by the NSERC-sponsored Metals in the Environment Research Network (MITE-RN, 1999-2004). In addition to the initial award, the MITHE-SN received an additional 2-year grant specifically targeted to (1) enhance training opportunities for internships with international organizations, (2) increase international networking and linkages, and (3) optimize knowledge dissemination and technology transfer. The research program is comprised of three themes and represents a cascade of effects along food webs, from the lowest trophic levels to the highest consumers. Each of the themes addresses issues related to distinguishing the magnitudes and roles of natural background and anthropogenic metal inputs in biotic exposure to metals; estimating the bioavailable fraction of metals in the exposure media, thus better quantifying the true exposure concentration; and determining the factors that influence bioavailability of metals in media, so that predictive models can be developed for use in the development of site-specific metals criteria.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".