Bibliographic record
Abstract
Old-School Phenol/Chloroform Genomic HMW DNA Preparation In order to mitigate damage/shearing of genomic DNA we have avoided kits etc. that employ beads or a matrix that your DNA must associate with or sieve through (blend :o)). We have not gone the whole hog at this point and used nuclei preps, dialysis or plug extractions etc. as we have found that material produced from a simple and rapid phenol/chloroform prep is more than adequate and high yielding. We have done some limited salting out experiments as a substitute for the phenol/chloroform approach but have some remaining questions around size and stability in the fridge for extended periods that need resolving. We will be revisiting this. The jumping off point for us was using methods detailed in “Molecular Cloning: A laboratory Manual” by Sambrooke and Russell. If you are at a large institution there will probably be copies around on people’s shelves or in the library collecting dust. It’s time to dust those off, they have been patiently waiting for their day in the sun again :o)). Chapter 6 is a good place to start. This approach produces DNA that is more than large enough for any nanopore sequencing currently.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.073 |
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".