Critical timelines and pathways: addressing environmental, social, governance and permitting
Bibliographic record
Abstract
The need for projects to focus on permitting and environmental, social and governance (ESG) considerations is increasingly prioritised over the technical and economic aspects of backfill and tailings solutions. This includes not only environmental permitting, but also takes into account public perception and responses to a project, as well as local community engagement and support. To optimise the success of a project, it is essential to incorporate and understand both the mining companies’ ESG frameworks and the local and governmental permitting requirements from the beginning. There is a need to pull these more advanced backfill and tailings designs earlier into the process to satisfy permitting requirements rather than the usual execution schedule. Permitting timelines may also lead to temporary backfill solutions such as cemented rockfill until paste backfill can be permitted and implemented. Comparing surface versus underground permitting requirements can lead to a more strategic approach by evaluating alternative solutions for infrastructure placement. For example, some facilities can be located underground if surface is perceived to be contentious. This can also lead to an approach where facility placement that might be better suited underground, but a more conservative approach is to start on surface and move underground as the project progresses. Given the focus on ESG and permitting and the timelines involved, it is more important than ever to provide a narrative which conveys a responsible integrated tailings solution for mining projects, both for permitting and to ensure public support.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".