Image-guided robot-assisted microscope objective lens positioning: Application in patch clamping
Bibliographic record
Abstract
There are applications where different objective lenses have to be used for microscope imaging. Rotary nose-pieces cannot be used when larger objectives are required and when there is a physical space limitation. It is also very difficult and time consuming to change the objective lens manually and locate and focus on the same spot again; This may prevent any attempt for automating an image-guided robot-assisted procedure using the microscope images with different objective lenses. A linear lens changing mechanism has been developed which makes it possible to slide the objectives under a microscope. Image processing algorithms have been used to determine the optimal position of the lenses with respect to the source of light, compensate for changes in the focal length in case of non-parfocal objectives and to locate and focus on the exact same spot, regardless of the objective change. A 3-DOF micromanipulator has been used to move the microscope with respect to the substrate. As one of the most challenging applications, this can facilitate objective lens change in computer-assisted patch clamping with multiple electrodes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".