Investigating the cytoskeleton of DRGs using cryo-electron microscopy and deep learning.
Bibliographic record
Abstract
The files associated with this post are raw data files and processed tomographic reconstructions that are the basis of figures presented in the manuscript "Investigating the cytoskeleton of DRGs using cryo-electron microscopy and deep learning." The data were collected on a Titan Krios operating at 300 keV with a BioQuantum Energy filter set at 20 eV in zero loss mode. Total dose for each tomogram was approximately 120 e-/A^2 and the pixel size of the raw data is 5.4 A/pixel. The etomo reconstructed tomomgrams were binned 4x so have a pixel size of 21.6 A/pixel. Files with _fractions.mrc and the associated .mdoc file should be moved into a single folder after downloading and the files can be drift-corrected and tilt series built using the script sort_and_alignv1_cap.sh run from within that folder. There are also a series of object files (*.ORSObject) that are exports from Dragonfly software (Comet Technologies, Montreal Canada). These are training datasets for building a neural network for segmentation with the Dragonfly encironment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".