Bibliographic record
Abstract
VADR Influenza model downloaded from https://ftp.ncbi.nlm.nih.gov/pub/nawrocki/vadr-models/flu/1.6.3-2/ From https://bitbucket.org/nawrockie/vadr-models-flu/src/master/00README.txt March 2024vadr-models-flu-1.6.3-2 https://github.com/ncbi/vadr VADR documentation:https://github.com/ncbi/vadr/blob/master/README.md Model download site:https://ftp.ncbi.nlm.nih.gov/pub/nawrocki/vadr-models/flu See 00RELEASE-NOTES.txt for details on changes between model versions. See 00NOTES.txt for additional information on the models. ------------ Recommended command for flu annotation using vadr v1.6.3(as of December 18, 2023) but still under testing and subject to change. v-annotate.pl \--split --cpu 4 -r \--atgonly --xnocomp --nomisc \--alt_fail extrant5,extrant3 \--mkey flu \--mdir \ The '--split --cpu 4' options will run v-annotate.pl multi-threaded on4 threads. To change to ' ' threads use '--split --cpu ', butmake sure you have * 4G total RAM available. To run single threaded remove the '--split --cpu 4' options. -- Contact eric.nawrocki@nih.gov for help.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.164 | 0.290 |
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".