The Progression and challenges in the implementation of a Waste Management System
Bibliographic record
Abstract
This paper presents insights generated from an ongoing client-pilot program that is exploring how the Integrated Tailings Management System (HITMS), a digitally integrated software system developed to improve the workflow of Tailings Management Facilities by connecting data throughout the Tailings lifecycle, leading to greater coordination and operational excellence.While the industry tends to focus on specific moments in the tailings lifecycle, the development and implementation journey of HITMS revealed that much stronger and distinct value can be generated by connecting all aspects of Tailings Management in real-time, from dewatering and transport to deposition and storage, combining the Global Industry Standard on Tailings Management (GISTM) and custom performance protocols.By implementing the system at two geographically and culturally unique sites, HITMS is able to showcase how it can address the distinct needs of one site, while also having standardization that allows for the understanding and eventual rollup for a portfolio of sites.HITMS provides a comprehensive suite of tools to manage, integrate, and visualize field data, monitor asset performance and operating thresholds, optimize job scheduling, and ensure improved regulatory compliance.The challenges encountered include data integration and migration, integration of existing workflows and systems, implementation of a holistic approach, user experience and resistance to change, amongst others.During implementation, digital and tailings technical teams engaged with the pilot clients with the aim to solve their operational challenges and ensure that the experience can be extended to other tailings facilities.As the system is being implemented, modules have been developed with the required functionalities to provide a flexible system that can be managed and customized by the user.
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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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".