Peel-1 negative selection promotes screening-free CRISPR-Cas9 genome editing in Caenorhabditis elegans.
Bibliographic record
Abstract
Raw and processed data for "Peel-1 negative selection promotes screening-free CRISPR-Cas9 genome editing in Caenorhabditis elegans." This includes data demonstrating the effectiveness of peel-1 negative selection on improving CRISPR-Cas9 genome editing, and functional data investigating the roles of two genes using Multi-Worm Tracker software. Further details about this project, including a README.md, paper abstract, and the R code necessary to regenerate all of the figures and formal analyses in the manuscript using the data in this repository can be found at: https://github.com/troymcdiarmid/peel-1 The "peel1_dels_0306_2020" folder includes all of the raw Multi-Worm Tracker (https://sourceforge.net/projects/mwt/) output files. The files are organized to have one folder per tracked plate (time stamped). The organized morphology summary (data.smorph) and reversal feature summaries (data.srev) are also included in case the user does not want to regenerate them using the code at the associated github (included above) The "peel_selection_data" folder includes raw data describing the effectiveness of peel-1 and selecting against extrachromosomal array transgenics during genome editing (see the "peel1_analysis" markdown document at the associated github page for further details).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.020 |
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".