Emergence of New Mechanical Functionality in Materials via Size Reduction
Bibliographic record
Abstract
Abstract Julia R. Greer received her S.B. in Chemical Engineering from the Massachusetts Institute of Technology (1997) and a Ph.D. in Materials Science from Stanford University, where she worked on the nanoscale plasticity of gold with W. D. Nix (2005). She also worked at Intel Corporation in Mask Operations (2000–03) and was a post‐doctoral fellow at the Palo Alto Research Center (2005–07), where she worked on organic flexible electronics with R. A. Street. Greer is a recipient of TR‐35, Technology Review's Top Young Innovator award (2008), a NSF CAREER Award (2007), a Gold Materials Research Society Graduate Student Award (2004), and an American Association of University Women Fellowship (2003). Julia joined Caltech's Materials Science department in 2007 where she is developing innovative experimental techniques to assess mechanical properties of nanometer‐sized materials. One such approach involves the fabrication of nanopillars with different initial microstructures and diameters between 25 nm and 1 µm by using focused ion beam and electron‐beam lithography microfabrication. The mechanical response of these pillars is subsequently measured in a custom‐built in situ mechanical deformation instrument, SEMentor, comprising a scanning electron microscope and a nanoindenter. Read our interview with Prof. Greer on MaterialsViews.com magnified image
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".