Investigation of spintronic materials systems: Deposition and characterization
Bibliographic record
Abstract
All I can hope to do here is give some small fraction of the appreciation due those who have helped carry this burden down the path for five years. I would first like to thank Tom McGill for being the ringleader of this circus, assembling under one tent a group whose like I can only hope in vain to work with again. He provided the means and environment to make this work happen and gave much more than he asked. Thanks to all the members of the SSDP group. Tim Harris kept the locomotive on track and gave moral support at times when it was sorely needed. Gerry Picus provided a lot of encouragement and was a great voice of reason. Bob Beach was one of the first to welcome me and is one of the best guys you can have to help you get started (or keep going). Xavier Cartoixà Solar cheerfully rode herd on us as only the sole theorist in a group of wrenchmonkeys can and showed us the power of multiple windows. Rob Strittmatter was very helpful on both the growth and device sides, and I hate to imagine the lab without him. Justin Brooke, postdoc extraordinaire, brought creativity and enthusiasm to the group and was the catalyst for much of this work. You are missed. Stephan Ichiriu, Master of Optics and Computers and Much Else, has been a great peer and a great help. I wish you the best in your future career. Cory Hill, worked closely and tirelessly with me from the beginning until now and his skill and importance in completing this work cannot be overestimated. He was helpful and dedicated above and beyond any obligation of mere employment. Ed Preisler’s energy, discipline, patience, spirit, and willingness to try new things were invaluable. I learned more than I can say from them. May the 49ers win five (more)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".