Additional file 1 of HostSeq: a Canadian whole genome sequencing and clinical data resource
Bibliographic record
Abstract
Additional file 1: Table S1. HostSeq Core Consent Elements. In order to deposit datasets in HostSeq COVID-19 controlled-access Databank, all the elements in this table must be obtained in the research consent. Table S2. HostSeq Case Report Form. Table S3. Software used for processing WGS data. Table S4. List of HostSeq participating studies as described in respective protocols. Table S5. Distribution of sex and age across HostSeq studies (n = 9,427). SD: Standard deviation; IQR: interquartile range. Figure S1. Quality of HostSeq genomes. (A) Missing rate < 5%, (B) Contamination rate < 3%, (C) Mean coverage >10. Figure S2. Predicted population admixture and ancestry classification in HostSeq genomes. Each bar represents a genome. Proportion of African, East Asian and European ancestries is determined, and genomes classified into 8 ancestry groups using GRAF-pop. They are further categorized into 5 superpopulations: AFR - African and African-American, AMR - Latin American Asian and Latin American African, EAS - Asian-Pacific Islander and East Asian, SAS - South Asian, and EUR - European. 3% of genomes remain uncategorized. Figure S3. Genetic distances score of HostSeq genomes. The four genetic distances (GD1-4) scores from GRAF-pop represent the distance of each genome from several reference populations and are used to predict ancestry. Barycentric coordinates of GD1 and GD2 are used to predict admixture proportion of African, East Asian and European ancestries.
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.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.787 | 0.190 |
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".