Bibliographic record
Abstract
Robotic Nanomaterial Manipulation inside scanning electron microscopes (SEM) is useful for prototyping functional devices and characterizing one-dimensional nanomaterialâs properties. Conventionally, manipulation of nanowires has been performed via teleoperation, which is time-consuming and highly skill-dependent. Manual manipulation also has the limitation of low success rates and poor reproducibility. This research focuses on a robotic system capable of automated pick-place of single nanowires. Through SEM visual detection and vision-based motion control, the system transferred individual silicon nanowires from their growth substrate to a microelectromechanical systems (MEMS) device that characterized the nanowiresâ electromechanical properties. The performances of the nanorobotic pick-up and placement procedures were quantified by experiments. The system demonstrated automated nanowire pick-up and placement with high reliability. A software system for a load-lock-compatible nanomanipulation system is also designed and developed in this research.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Robotic nanomanipulation under electron microscopy; 'reproducibility' here refers to a manipulation procedure, a polysemy trap.
It develops and evaluates automated nanowire manipulation technology, with reproducibility referring to process performance.
Automated SEM nanomanipulation is materials/robotics engineering, not metaresearch.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".