Bibliographic record
Abstract
Oak Ridge National Laboratory hosted two workshops in April 2007 relevant to nuclear engineering education. In the Neutron Stress, Texture, and Phase Transformation for Industry workshop [http://neutrons.ornl.gov/workshops/nst2/], several invited speakers gave examples of neutron stress mapping for nuclear engineering applications. These included John Root of National Research Council of Canada, Mike Fitzpatrick of the UK's Open University, and Yan Gao of GE Global Research on their experiences with industrial and academic uses of neutron diffraction. Xun-Li Wang and Camden Hubbard described the new instruments at ORNL that can be used for such studies. This was preceded by the Neutrons for Materials Science and Engineering educational symposium [http://neutrons.ornl.gov/workshops/edsym2007]. It was directed to the broad materials science and engineering community based in universities, industry and laboratories who wish to learn what the neutron sources in the US can provide for enhancing the understanding of materials behavior, processing and joining. Of particular interest was the presentation of Donald Brown of Los Alamos about using 'Neutron diffraction measurements of strain and texture to study mechanical behavior of structural materials.' At both workshops, the ORNL neutron scattering instruments relevant to nuclear engineering studies were described. The Neutron Residual Stress Mapping Facility (NRSF2) is currently inmore » operation at the High Flux Isotope Reactor; the VULCAN Engineering Materials Diffractometer will begin commissioning in 2008 at the Spallation Neutron Source. For characteristics of these instruments, as well as details of other workshops, meetings, capabilities, and research proposal submissions, please visit http://neutrons.ornl.gov. To submit user proposals for time on NRSF2 contact Hubbard at hubbardcr@ornl.gov.« less
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".