JoVE Video Dataset
Bibliographic record
Abstract
This method is used to extract high molecular weight genomic DNA from planktonic biomass concentrated on 0.22 μM Sterivex filters that have been treated with storage/lysis buffer and archived at -80°C, and to purify this DNA using a cesium chloride density gradient. The protocol begins with two one-hour incubation steps to liberate DNA from cells and remove RNA. Next, a series of Phenol:Chloroform and Chloroform extractions are performed followed by centrifugation to remove proteins and cell membrane components, collection of the aqueous DNA extract, and several buffer exchange steps to wash and concentrate the extract. Part Five describes the optional purification via cesium chloride density gradient. It is recommended to work with less than 15 samples at one time to avoid confusion and cut down protocol time. The total time required for this protocol depends on the number of samples to be extracted. For 10-15 samples and assuming the proper centrifugation equipment is available, this entire protocol should take 3 days. Make sure you have the hybridization ovens set to temperature at the outset of the process.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.230 | 0.193 |
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".