Feature Detection, Characterization and Confirmation Methodology: Final Report
Bibliographic record
Abstract
This is the final report of the NUMO-LBNL collaborative project: Feature Detection, Characterization and Confirmation Methodology under NUMO-DOE/LBNL collaboration agreement, the task description of which can be found in the Appendix. We examine site characterization projects from several sites in the world. The list includes Yucca Mountain in the USA, Tono and Horonobe in Japan, AECL in Canada, sites in Sweden, and Olkiluoto in Finland. We identify important geologic features and parameters common to most (or all) sites to provide useful information for future repository siting activity. At first glance, one could question whether there was any commonality among the sites, which are in different rock types at different locations. For example, the planned Yucca Mountain site is a dry repository in unsaturated tuff, whereas the Swedish sites are situated in saturated granite. However, the study concludes that indeed there are a number of important common features and parameters among all the sites--namely, (1) fault properties, (2) fracture-matrix interaction (3) groundwater flux, (4) boundary conditions, and (5) the permeability and porosity of the materials. We list the lessons learned from the Yucca Mountain Project and other site characterization programs. Most programs have by and large been quite successful. Nonetheless, there are definitely 'should-haves' and 'could-haves', or lessons to be learned, in all these programs. Although each site characterization program has some unique aspects, we believe that these crosscutting lessons can be very useful for future site investigations to be conducted in Japan. One of the most common lessons learned is that a repository program should allow for flexibility, in both schedule and approach. We examine field investigation technologies used to collect site characterization data in the field. An extensive list of existing field technologies is presented, with some discussion on usage and limitations. Many of the technologies on the list were in fact used during the characterization of Yucca Mountain and elsewhere by LBNL personnel. The study also includes emerging technologies and identifies the need to develop better estimation of important parameters for repository siting. Notable emerging technologies include 3-D seismic and satellite-based remote sensing and wireless micro electro mechanical systems (MEMS) sensors. They enable cost-effective and ubiquitous monitoring to be applied for site characterization. We list and classify the types of uncertainties involved in site characterization. Uncertainties can exist in all aspects of site characterization: data, interpretation, conceptualization, and modeling. We use the Swedish program to exemplify such uncertainties. We also devote a chapter on geochemical issues regarding the interaction between groundwater and natural and engineered barrier materials. A recommendation has been made to take advantage of the recent advancement in geochemical modeling capabilities in natural systems. Although it is not of immediate relevance at the preliminary investigation stage, it serves as a good reminder that geochemical investigation efforts should not be overlooked at any stage in the repository program. We construct a synthetic preliminary-investigation site based on an extensive data set available from a geoscientific project in Japan, which we use as a 'real' site to evaluate uncertainties resulting from hydrogeological modeling and examine strategies for characterizing a new site. We plan various preliminary-investigation configurations and conduct preliminary numerical investigations at the synthetic site. We construct a model of the 'real' site for each PI configuration, make predictions of particle travel times, and compare against the 'real' data obtained from the 'real' model. We conclude that drilling as many as nine boreholes does not necessarily improve the understanding of the site compared to drilling as few as three boreholes, unless there is an underlying structure that is larger than the spacing of the boreholes. The parameters that affect the outcome of the predictions most are: (1) effective porosity, (2) boundary conditions, and (3) fault properties, all of which are very difficult to estimate in the field and are full of uncertainties. Of the three, we recommend NUMO expend efforts to assess the latter two at preliminary investigation sites. To obtain large-scale averaged permeabilities, we recommend conducting long-time and long-interval pumping tests in boreholes. We also find that the temperature data can reduce some uncertainties regarding the boundary conditions. Finally, we summarize recommendations that NUMO might consider during preliminary site investigations.
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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 | 0.041 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.133 |
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".