Michigan International Copper Analogue (MICA) project – current status
Bibliographic record
Abstract
Abstract. One of the key requirements for the deep geological disposal of high-level nuclear waste is the assessment of its long-term performance and safety (up to 1 Ma). Regarding engineered barrier system materials, such as copper, much of the data available comes from short-term investigations, such as laboratory experiments at different scales. Copper is an important part of many waste packaging and disposal concepts, e.g. KBS-3 developed in Sweden and Finland and Mark II developed in Canada. Natural analogues provide another important way of obtaining understanding on potential repository system behavior. Observations made from the geological systems can be utilized in the safety case, providing information on the assessment time scale. Copper analogue studies (both natural analogues and archaeological analogues) have been reported in the literature and they have been extensively reviewed by various authors (e.g. Miller et al., 2000) and by safety case projects (e.g. Reijonen et al., 2015) within waste management organizations. So far, only a few studies have focussed on the general stability of native copper within its natural media (e.g. Milodowski et al., 2000; Marcos, 2002). Keweenaw native copper occurrences (Lake Superior, USA) have been mentioned as a qualitative source of information (e.g. in Miller et al., 2000); however, data to be used in process-based safety assessments for geological disposal are lacking. These deposits have been mined for a long time and there is a great deal of knowledge related to them as well as samples collected, but no formal review has been made from the geological disposal point of view. The native copper at the Keweenaw area reflects various geological environments from bedrock to sediment and even anthropogenic mine site remnants and geochemical environments (e.g., anoxic vs. oxic, sulphur-free vs. sulphur-bearing). It thus provides a unique complementary data source that will be useful for estimating processes governing behavior of metallic copper. The MICA project phase I systematically collects and reviews the existing literature and data on the Michigan copper analogue sites and available sampling potential. Here, we present the current status of the project.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.018 |
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".