Sequencing data and MLPA analysis data in support of the effectiveness and reliability of an asymmetric PCR-Based approach in preparing long MLPA probes
Bibliographic record
Abstract
ABI PRISM 3100 Genetic Analyzer, a multi-color fluorescence-based DNA analysis system with 16 capillaries operating in parallel, was ideal tool both for DNA sequencing and DNA fragment analysis [1,2]. To demonstrate the effectiveness and reliability of an asymmetric PCR-Based approach (X.Y. Ling, G.M. Zhang, G. Pan, H. Long, Y.H. Cheng, C.Y. Xiang, L. Kang, F. Chen, Z.N. Chen, Preparing long probes by an asymmetric PCR-based approach for multiplex ligation-dependent probe amplification (MLPA), Anal. Biochem. (2015), http://dx.doi.org/10.1016/j.ab.2015.03.031, in press) in preparing the long MLPA probes that were generated with a M13-based method before [4], some prepared long MLPA probes were sequenced and then tested in MLPA analysis. Sequencing data shows that the long MLPA probes were identical to the designed ones, indicating the long probes can be easily prepared with the new method, and the MPLA analysis data shows that the results of MPLA analysis with these long probes were as same accurate and specific as with ones prepared with other methods. The sequencing data was not presented in the research article (X.Y. Ling, G.M. Zhang, G. Pan, H. Long, Y.H. Cheng, C.Y. Xiang, L. Kang, F. Chen, Z.N. Chen, Preparing long probes by an asymmetric PCR-based approach for multiplex ligation-dependent probe amplification (MLPA), Anal. Biochem. (2015), 10.1016/j.ab.2015.03.031, in press), but the MLPA analysis data was converted into figure 4 and figure 5 of the research article.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".