Data Analysis files for "Emergent Macroscopic Bistability Induced by a Single Superconducting Qubit"
Bibliographic record
Abstract
Data analysis files for the manuscript "Emergent Macroscopic Bistability Induced by a Single Superconducting Qubit". This contains the raw data and the data analysis files for generating the figures in the manuscript. Figure1 file - The raw data of cavity transmission spectra for 6 different kappas are there. They are fitted with input-output theory in the python file. Figure2 file - The raw data at 8 MHz kappa are included. all hte figures in figure 2 are generated in the python file Figure3 file - The raw data of PBB single shot measurements at all kappas are included. The detailed analysis and the Figure3 generated for the paper are all in the python analysis file. Also, thefiles containing the time-evolution of the intensity from Master Equation solution are included. Figure4 file - The raw data at 2.6 MHz for different drive detunings and the corresponding analyses are included. And the python file includes the analysis of the experimental data as well as approximate neoclassical equations solutions for 2-level and 3-level transmons are included.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.130 | 0.112 |
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".