Learning from 1 billion year old copper
Bibliographic record
Abstract
In Canada, the Nuclear Waste Management Organization (NWMO) is responsible for the long-term management of spent nuclear fuel, which involves sealing used fuel bundles in copper-coated carbon steel used fuel containers (UFC) and emplacing them ~500 m underground in a deep geological repository (DGR). In this plan, copper plays a vital role in ensuring the safety of the DGR as it is intended to serve as a corrosion barrier for greater than one million years. Veins and pods of natural copper have long been known to occur in the Lake Superior region of North America where they have been culturally significant to Indigenous peoples for millennia. The natural Lake Superior copper deposits were emplaced close to one billion years ago in lithosphere which has since had a protracted history of glacial overrides and related isostatic adjustment events. In light of this longevity, structural history and exposure to crustal fluids, it has the potential to hold many lessons for DGR specialists and society in general. We present two aspects of our approach. The first is an outline of our efforts to increase cultural competency of non-indigenous scientists in our group while building an understanding of the Indigenous Knowledge system and how it can be respectfully and effectively applied to research. This includes respectful ways in which to collect, and learn from, copper samples. This work directly relates to the important relationships in the context of the NWMO Indigenous Knowledge policy, Reconciliation policy and how both policies apply to the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). In this context we will also present our early field and laboratory observations of natural copper properties through an integrated analysis of chemical and orientation microstructure in tandem with electrochemical behaviour. Techniques in the laboratory workflow include electron microscopy (EDS, EBSD) and atom probe tomography to map impurities and microstructure relative to manufactured wrought copper (phosphorus-doped oxygen free); Auger electron and X-ray photoelectron spectroscopies to determine surface and near-surface composition and chemical environment; and electrochemical methods such as corrosion potential measurements and potentiodynamic polarization scans to probe the corrosion performance. These measurements may inform container design aspects such as optimal fabrication and the role of impurities in corrosion behaviour of electrodeposited and cold spray-deposited copper in a DGR over geological timescales. Taken together, our aim is to come to an appropriately comprehensive understanding of the cultural, geological and material corrosion aspects of natural copper that has persisted for a time span three orders of magnitude greater than the DGR design requirements. It is our hope that this learning approach to ancient natural copper will play a positive role in seeking social license for DGR planning, while having value for societal education in the global challenge of geostorage.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".