Additional file 1 of Genomic classification and antimicrobial resistance profiling of Streptococcus pneumoniae and Haemophilus influenza isolates associated with paediatric otitis media and upper respiratory infection
Bibliographic record
Abstract
Additional file 1: Table S1. Clinical metadata. Table S2. Kraken 2 contig classifications weighted by contig length (% total assembly size) for S. pneumoniae isolates. Table S3. Assembly statistics from Pathogenwatch for S. pneumoniae isolates. Table S4. Serotype, MLST sequence typing, AMR profile, and select clinical metadata for S. pneumoniae isolates. Table S5. Additional AMR predictions compared to clinical metadata for S. pneumoniae isolates. Table S6. Penam resistance hits from CARD results. Table S7. FastANI results for S. pneumoniae isolate 1001 compared to NCBI-Genbank S. pneumoniae strains. Table S8. Sequence type, serotype and AMR profile for S. pneumoniae isolate 1001's closest related S. pneumoniae strains. Table S9. VFDB comparison of virulence factor genes across select S. pneumoniae strains. Table S10. Kraken 2 contig classifications weighted by contig length (% total assembly size) for H. influenzae isolates. Table S11. Assembly statistics from Pathogenwatch for H. influenzae isolates. Table S12. MLST sequence typing and select clinical metadata for H. influenzae isolates. Table S13. Beta-lactamase hits from CARD results.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.345 | 0.099 |
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".