A study of comparability in amplified fragment length polymorphism profiling using a simple model system
Bibliographic record
Abstract
A simple amplified fragment length polymorphism (AFLP) model, using the bacteriophage lambda genome, was developed to test the reproducibility of this technique in an international comparative study. Using either non-selective or selective primers, nine fragments or subsets of two or three fragments, respectively, were predicted using in silico software. Under optimized conditions, all predicted fragments were experimentally generated. The reproducibility of the AFLP model was tested by submitting both "unknown" DNA template that had been restricted and ligated with AFLP linkers (R/L mixture) and corresponding primer pairs to nine laboratories participating in the study. Participants completed the final PCR step and then used either slab gel electrophoresis or CE to detect the AFLP fragments. The predicted fragments were identified by the majority of participants with size estimates consistently up to 3 base pair (bp) larger for slab gel electrophoresis than for CE. Shadow fragments, 3 bp larger than the predicted fragments, were often observed by study participants and organizers. The nine AFLP fragments exhibited relative intensities ranging from less than 3% to 22% and, apart from the two weakest fragments, with a % CV of 16 to 25. Fragments containing the highest guanine-cytosine (GC) content of 50-56% showed the greatest stability in the AFLP profiles.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".