Canine atopic dermatitis datasets
Bibliographic record
Abstract
The datasets consists of genotype data from dogs in plink format. ATOPYK2 plink files contains genotype data from five dog breeds with and without atopic dermatitis. The ATOPYK2 datafiles were used for imputation. These are pre-QC:ed with the following plink settings --maf 0.001 --geno 0.05 --mind 0.05 #We recommend a second qc before doing the imputation. The imputation was then run per chromosome so for redoing the imputation, as was done in the publication to which this data is connected, use the following plink code: plink --bfile ATOPYK2 --chr $chrN --make-bed --dog --allow-no-sex --keep-allele-order --maf 0.001 --geno 0.2 --out 'ATOPYK.chr'$chrN #The final datasets after imputation are datasets split breed wise and in plink format. These sets are QC:ed, and relatedness filtered. The "covar"-file includes fixed effects, these are different between depending on what covariates were significantly affecting the trait (atopic dermatitis) in each breed. These datasets are named GR (golden retriever), LR (labrador retriever), GSD (german shepherd) and WHWT (west highland white terrier). (Boxers are in the ATOPYK2 files but not used for final analyses, hence no breed specific datafile for boxer are included).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.037 |
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".