Homosexualidad e intertextualidad en los amores del Barón de Charlus
Bibliographic record
Abstract
Here, we introduce <i>VLF</i>, an R package to determine the distribution of very low frequency variants (VLFs) in nucleotide and amino acid sequences for the analysis of errors in DNA sequence records. The package allows users to assess VLFs in aligned and trimmed protein-coding sequences by automatically calculating the frequency of nucleotides or amino acids in each sequence position and outputting those that occur under a user-specified frequency (default of <i>p</i> = 0.001). These results can then be used to explore fundamental population genetic and phylogeographic patterns, mechanisms and processes at the microevolutionary level, such as nucleotide and amino acid sequence conservation. Our package extends earlier work pertaining to an implementation of VLF analysis in Microsoft Excel, which was found to be both computationally slow and error prone. We compare those results to our own herein. Results between the two implementations are found to be highly consistent for a large DNA barcode dataset of bird species. Differences in results are readily explained by both manual human error and inadequate Linnean taxonomy (specifically, species synonymy). Here, <i>VLF</i> is also applied to a subset of avian barcodes to assess the extent of biological artifacts at the species level for Canada goose (<i>Branta canadensis</i>), as well as within a large dataset of DNA barcodes for fishes of forensic and regulatory importance. The novelty of <i>VLF</i> and its benefit over the previous implementation include its high level of automation, speed, scalability and ease-of-use, each desirable characteristics which will be extremely valuable as more sequence data are rapidly accumulated in popular reference databases, such as BOLD and GenBank.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".