Specific heat in the fractional quantum Hall regime
Bibliographic record
Abstract
First and foremost, I thank Professor Guillaume Gervais for his unfailing confidence in me throughout my PhD.I loved every second I spent in the lab -whether soldering circuits, installing pumps, sketching on the whiteboard or endlessly transferring liquid helium into the insatiable refrigerator.None of this would have been possible without his unfaltering support.In addition to the many scientific discussions I had with Guillaume, I also benefitted from conversations with a number of other faculty members, including Thomas Szkopek and Aashish Clerk at McGill, Kun Yang at the National High Magnetic Field Laboratory, and an endless stream of visitors to the lab and fellow conference-goers.These numerous opportunities to discuss openly my work and "talk shop" with so many fantastic researchers was one of the great aspects of the research environment that Guillaume was able to provide.To perform any of my experiments, I first needed samples to measure.I am grateful to Loren Pfeiffer and Ken West at Princeton University and John Reno and Dominique Laroche at Sandia National laboratories for providing samples.Even though I had the best samples available, I would not have gotten anywhere in the lab without the help of technicians/miracle-workers, John Smeros, Richard Talbot and Robert Gagnon.I also benefitted greatly from the assistance of Juan Galego to keep the computers running and Pascal Bourseguin in the machine shop.I am also thankful to the rest of the physics department faculty and staff, who in various ways facilitated my research, enriched my studies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".