Helping Incorporate Safe and Sustainability into Materials Research: A Checklist Tool Designed for Early Career Researchers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Chemicals and materials with essential and functional uses are central to economic and social well-being. However, the development and application of such items may be challenged when sustainability factors are considered. These are further challenged by disciplinary gaps and concerns over reproducible and responsible research. Here, we detail a checklist for early career researchers on how “to do” research on chemicals, materials, processes, and products in a manner that is safe and sustainable by design. The checklist contains 20 items that are organized into five interconnected sections: (1) think broad and big, (2) set a clear research foundation, (3) focus on quality and reproducibility, (4) flag safe and sustainability issues, and (5) communicate, listen, and learn. Each item can be self-scored on 0–3 scale. This checklist is meant to be a fast tool to help researchers better “think and do” things so that outcomes and outputs may help address societal grand challenges.
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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 | 0.060 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".