Holographic models of closure landscapes for stakeholder engagement: when you need more than words and pictures
Bibliographic record
Abstract
The Giant Mine Remediation Project (Giant) and Faro Mine Remediation Project (Faro) are abandoned mine projects in northern Canada with long legacies and many complex challenges. BGC Engineering Inc. has been working with Crown-Indigenous Relations and Northern Affairs (CIRNAC) using holographic projections to collaborate with First Nations and local communities on key mine closure information. This paper shares experience with using Microsoft’s mixed reality HoloLens devices (HoloLens) as a tool that creates a safe and equitable environment to collaboratively engage with communities where previous experience and technical understanding are not prerequisites to contributing. This is important, as engaging in meaningful discussions involving mine closure planning involves effectively communicating engineering and earth science information to rights holders and stakeholders with diverse experiences. The Faro project is beginning to develop several 3D digital models to encourage conversation, discussion, and engagement on the progress of mine closure, showcasing a time lapse of how the mine has changed over the past few years and its current state, and to explore the staged approach to reclamation. HoloLens was used to incorporate a broad spectrum of data into holographic models that were shared with multiple users to experience together. The digital models incorporated historical topographic and aerial imagery and current conditions, and include proposed future conditions based on designs and reclamation plans. The participants can collaboratively experience this collated information from different viewpoints, including in tabletop models, as 3D objects in the middle of the room, and as immersive 3D scenes. At Giant, there is a complex underground mining legacy that the community wanted to better understand. Holographic models were created that allow users to visualise and walk around the surface and underground together, as a community, and ask questions, voice concerns and ideas, and see how groundwater conditions change throughout the seasons and into the future. Additionally, as there are several locations throughout the mine that will be reclaimed based on input from the community, it is important to be able to visualise the design beyond 2D engineering-based drawings. Real-life-scale immersive views were created, providing experiences of how these areas might look once reclamation is finished. We hope that by sharing the experience of sharing information in this way that the state of practice for creating holistic and sustainable mine closure designs that reflect the input of rights holders and stakeholders can be advanced.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".