DoGSD: the dog and wolf genome SNP database
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
The rapid advancement of next-generation sequencing technology has generated a deluge of genomic data from domesticated dogs and their wild ancestor, grey wolves, which have simultaneously broadened our understanding of domestication and diseases that are shared by humans and dogs. To address the scarcity of single nucleotide polymorphism (SNP) data provided by authorized databases and to make SNP data more easily/friendly usable and available, we propose DoGSD (http://dogsd.big.ac.cn), the first canidae-specific database which focuses on whole genome SNP data from domesticated dogs and grey wolves. The DoGSD is a web-based, open-access resource comprising ∼ 19 million high-quality whole-genome SNPs. In addition to the dbSNP data set (build 139), DoGSD incorporates a comprehensive collection of SNPs from two newly sequenced samples (1 wolf and 1 dog) and collected SNPs from three latest dog/wolf genetic studies (7 wolves and 68 dogs), which were taken together for analysis with the population genetic statistics, Fst. In addition, DoGSD integrates some closely related information including SNP annotation, summary lists of SNPs located in genes, synonymous and non-synonymous SNPs, sampling location and breed information. All these features make DoGSD a useful resource for in-depth analysis in dog-/wolf-related studies.
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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) | 0.000 | 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 | 0.001 |
| 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