Bibliographic record
Abstract
This dataset contains raw and processed data for "Assessing Markovian and delay models for single-nucleus RNA sequencing". allen_delay_loom.tar.gz: Spliced/unspliced RNA count data obtained by pseudoaligning mouse brain datasets B02 and A02 from the Allen Institute for Brain Science, stored in loom files. liver_c72_delay_loom.tar.gz: Spliced/unspliced RNA count data obtained by pseudoaligning single-cell and single-nucleus human liver datasets (donor C72) from Toronto’s University Health Network, stored in loom files. search_data_objects.tar.gz: outputs of the Monod pipeline, including search data and results. smc_fits.tar.gz.*: search results, compressed and split by tar czvf - smc_fits/ | split --bytes=500MB - smc_fits.tar.gz. This directory contains raw (full PyMC3 objects) as well as filtered (extracted traces and likelihoods) search result objects. gyp_2022-zenodo.zip: mirror of the GitHub repo.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.335 | 0.347 |
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".