Electron microscopy cell fraction preparation robot
Bibliographic record
Abstract
We have developed a high-throughput device to expedite and standardize cell fraction sample preparation for electron microscopic examination. It provides a means for mass, parallel validation of sub-cellular sample purity and confirmation of protein localization in isolated organelles. Due to the inherent fragile nature of cell fraction specimens, the device was designed to handle all aspects of chemical and mechanical manipulation necessary to prepare organelles for electron microscopic examination. Its modular design permits sequential, automated filtration, chemical processing, delivery and embedding of 96 cell fraction samples in parallel. The automated system minimizes mechanical stress to the samples, controls delivery and removal of processing reagents and regulates temperature by integrating five sub-systems: (1) a core mechanism composed of four modular plates, (2) a 4-axis modon control system (X, Y, Z, /spl theta/), (3) an electromagnetic plate transfer arm, (4) a cooling platform, and (5) an automated fluids handling sub-system. As part of the supporting technology developing for proteomics, the automated device allows, for the first time, massive, parallel electron microscopy screening and subsequent statistical analysis of sub-cellular and protein targets necessary for high-throughput proteomics.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".