Ten years later: The progress and future of integrating sustainable principles, practices, and metrics into remediation projects
Bibliographic record
Abstract
Abstract In 2009, the Sustainable Remediation Forum released a white paper entitled “Integrating sustainable principles, practices, and metrics into remediation projects” (Ellis & Hadley, 2009, Remediation, 19, pp. 5–114). Sustainable remediation was a relatively new concept, and the white paper explored a range of approaches on how sustainability could be integrated into traditional remediation projects. This paper revisits the 2009 white paper, providing an overview of the early days of the evolving sustainable remediation practice and an assessment of the progress of sustainable remediation over the last 10 years with a primary focus on the United States. The current state of the sustainable remediation practice includes published literature, current practices and resources, applications, room for improvement, international progress, the virtuous cycle that applying sustainable remediation creates, and the status of the objectives cited in the 2009 white paper. Over the last decade, several sustainable remediation frontiers have emerged that will likely be a focus in advancing the practice. These frontiers include climate change and resiliency, weighting and valuation to help better consolidate different sustainable remediation metrics, programmatic implementation, and better integration of the societal impacts of sustainable remediation. Finally, as was the case for the 2009 white paper, this paper explores how sustainable remediation may evolve over the next 10 years and focuses on the events and drivers that can be significant in the pace of further development of the practice. The events and drivers include transformation impacts, societal influences, and the continued development of new technologies, approaches, and tools by remediation practitioners. The remediation industry has made significant progress in developing the practice of sustainable remediation and has implemented it successfully into hundreds of projects. While progress has been significant, an opportunity exists to implement the tenets of sustainable remediation on many more projects and explore new frontiers to help improve the communication, integration, and derived benefits from implementing sustainable remediation into future remediation projects.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".