Bibliographic record
Abstract
Viral ImmunologyVol. 34, No. 4 EditorialHerd Immunity Against COVID-19: More Questions Than AnswersRodney S. RussellRodney S. RussellAddress correspondence to: Dr. Rodney S. Russell, BioMedical Sciences, Memorial University of Newfoundland, 300 Prince Philip Dr., St. John's, Newfoundland and Labrador A1C 5S7, Canada E-mail Address: [email protected]BioMedical Sciences, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Canada.Search for more papers by this authorPublished Online:13 May 2021https://doi.org/10.1089/vim.2021.0075AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View article"Herd Immunity Against COVID-19: More Questions Than Answers." Viral Immunology, 34(4), pp. 211–212FiguresReferencesRelatedDetails Volume 34Issue 4May 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Rodney S. Russell.Herd Immunity Against COVID-19: More Questions Than Answers.Viral Immunology.May 2021.211-212.http://doi.org/10.1089/vim.2021.0075Published in Volume: 34 Issue 4: May 13, 2021PDF download
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".