Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whole-genome shotgun sequencing is an experimental technique used for obtaining information about a genomeâs sequence, whereby it is broken up into many short (possibly overlapping) segments whose sequence is then determined. A long-standing use of sequencing is in genome assembly â the problem of determining the sequence of an unknown genome, which plays a central role for the sequencing of novel species. However, even within the same species, the genomes of two individuals differ, and though these variations are relatively small, they account for the observed variation in phenotypes. A large portion of these are copy number variants (CNVs), or genomic segments which appear a different number of times in different individuals.\n The unifying theme of this thesis is the use of genome graphs for both CNV detection and genome assembly problems. Genome graphs, which have already been successfully used for alignment and assembly, capture the structure of a genome even when its sequence is not fully known, as with the case of sequencing data. In this thesis, we extend\ntheir uses in several ways, culminating in a method for CNV detection that is based on a novel genome graph model. First, we demonstrate how the double-stranded nature of\nDNA can be efficiently incorporated into genome graphs by using the technique of bidirected network flow. Furthermore, we show how genome graphs can be efficiently used for finding solutions that maximize the likelihood of the data, as opposed to the usual maximum parsimony approach. Finally, we show how genome graphs can be useful for\nCNV detection through a novel construction called the donor graph. These extensions are combined into a method for detecting CNVs, which we use on a Yoruban human individual, showing a high degree of accuracy and improvement over previous methods.
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.
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 | 0.000 | 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 it