Metals in the Environment: Philosophy and Action by the Metals Industry
Bibliographic record
Abstract
A critical issue facing our industrial society is to determine how to continue the beneficial use of metals while minimizing adverse effects metal releases may have on people or the environment. The best way to examine potential adverse effects is to carry out risk assessments. The metals resource industry and certain federal government departments have taken a proactive approach to gaining needed information for risk assessments on metals by forming the Metals in the Environment (MITE) Research Network. This Network, receiving significant funding from NSERC for university research across Canada, is administering a 5-year integrated program defined by government and industry in a truly co-operative and integrated fashion. The program is focussing on: sources of metals, both industrial and natural; processes that move and control metal species; and impacts of metals on flora and fauna. Resume Continuer a utiliser les metaux tout en minimisant les effers nefastes d'effluents de metaux pour les humains et l'environnement constitue l'un des principaux defis de notre societe industrialisee. La meilleure approche permettant d'etudier les effets nefastes potentiels consiste a mener des etudes d'evaluation des risques. L'industrie des ressources metalliques avec certains ministeres du gouvernement federal ont adopte une approche proactive dans le but d'amasser les donnees necessaires aux evaluations des risques des effluents de metaux et ils ont cree le Reseau de recherche des metaux dans l'environnement (RRME). Dote d'un financement substantiel par le CRSNG, le RRME administrera un programme quinquennal pan-canadien de recherches universitaires de maniere veritablement integree et cooperative. Ce programme porte en particulier sur les sources des metaux, naturelles ou industrielles, les processus de transport et de selection des especes de metaux, ainsi que l'impact des metaux sur la flore et la faune.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".