Correction: Suspension of oral hygiene practices highlights key bacterial shifts in saliva, tongue, and tooth plaque during gingival inflammation and resolution
Bibliographic record
Abstract
Authors and Affiliations Faculty of Graduate Studies, Dalhousie University, Halifax, NS, Canada Michael William Hall Faculty of Dentistry, University of Toronto, Toronto, ON, Canada Nimali Chandhema Wellappuli, Ruo Chen Huang & Michael Glogauer McMaster University, Hamilton, ON, Canada Kay Wu University of the Pacific, San Francisco, CA, USA David King Lam Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada Robert Gerald Beiko Stony Brook University, School of Dental Medicine, Stony Brook, NY, USA Dilani Braziunas Senadheera Authors Michael William Hall View author publications You can also search for this author in PubMed Google Scholar Nimali Chandhema Wellappuli View author publications You can also search for this author in PubMed Google Scholar Ruo Chen Huang View author publications You can also search for this author in PubMed Google Scholar Kay Wu View author publications You can also search for this author in PubMed Google Scholar David King Lam View author publications You can also search for this author in PubMed Google Scholar Michael Glogauer View author publications You can also search for this author in PubMed Google Scholar Robert Gerald Beiko View author publications You can also search for this author in PubMed Google Scholar Dilani Braziunas Senadheera View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Dilani Braziunas Senadheera .
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 | 0.002 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.052 | 0.026 |
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".