Solid Phase DNA Amplification: A Simple Monte Carlo Lattice Model
Bibliographic record
Abstract
ABSTRACT Recently, a new way to amplify DNA, called solid phase amplification (SPA), has been introduced. SPA differs from the traditional polymerase chain reaction (PCR) in the use of surface-bound instead of freely-diffusing primers to amplify DNA. This limits the amplification to two-dimensional surfaces and therefore allows the easy parallelization of DNA amplification in a single system. Furthermore, SPA could provide an alternate route to DNA target implantation on DNA chips for genomic studies. Standard PCR processes are usually characterized (at least initially) by an exponential growth and a broad population distribution, and they are well described by the theory of branching processes, wherein a generating function can be used to obtain the probability distribution function for the population of offspring. This theoretical approach is not appropriate for SPA because it cannot properly take into account the many-body (steric) and geometric effects in a quenched two-dimensional environment. In this article, we propose a simple Lattice Monte Carlo technique to model SPA. We study the growth, stability, and morphology of isolated DNA colonies under various conditions. Our results indicate that, in most cases, SPA is characterized by a geometric growth and a rather sharp size distribution. Various non-ideal effects are studied, and we demonstrate that such effects do not generally change the nature of the process, except in extreme cases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".