Bibliographic record
Abstract
Feedback traps can manipulate particles arbitrarily. In a feedback trap, a position detector detects the particle’s position, a computer calculates the necessary force to be applied based on the position in the “virtual potential,” which is applied to the particle. The process is repeated with as fast a loop rate as practical. Previous feedback traps have used electrokinetic or hydrodynamic forces to manipulate particles. Here, an optical trap creates the force used by the feedback trap to impose arbitrary potentials. We create feedback forces on optically trapped particles by moving the trap position rapidly in response to observed fluctuations with the help of an acoustooptic deflector (AOD). In preliminary experiments, we have confined a 1.5 μm silica bead in a virtual potential that is 35-40 times stiffer than the underlying optical trap, whose laser power is kept constant. We also create a virtual double-well potential with independent control over the well separation and barrier height, which is impossible to do with time-sharing optical tweezers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".