Quantitative Models for Efficient Cloning of Different Vectors with Various Clone sites
Bibliographic record
Abstract
We developed an efficient "Combinatorial Strategy” for cloning different vectors with various clone sites. 1) Using originally existed clone sites from circular vectors to prepare the inserts, and if no appropriate sites are available, performing SDM to create compatible sites, to achieve maximal correct digestion of the inserts. 2) Different vectors were digested with various restriction endonucleases, and then dephosphorylated with CIP. 3) Top10 competent cells were used for transformation to increase the transformant colonies. Our results showed that, when blunt sites, or a Xba I site was adopted for ligation, the percentages of positive clones were about 50%. Whereas, when different sites, including one blunt and another Pst I sites, Not I and Xho I sites, the percentages of positive clones were nearly 100%. Using this strategy, most vectors could be successfully cloned through “one ligation, one transformation, three to five minipreps."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".