<i>In situ</i>substrate-formed biofilms using IDODS mimic supragingival tooth-formed biofilms
Bibliographic record
Abstract
to a supragingival tooth-formed biofilm (TF-biofilm) in the same group of individuals. The impact of the device/disc position and toothbrushing during the formation of SF-biofilm was also assessed. Two tests were run. In test 1, 15 volunteers wore two hemi-splints carrying six discs of human enamel, glass, and hydroxyapatite for 2 days, and were instructed to not perform any oral hygiene measure. Biofilm samples were collected from the substrates and the contralateral tooth and were analysed using CLSM. In five volunteers, half of the biofilm present on the discs and their contralateral teeth were scraped and analysed using 16S pyrosequencing. In test 2, the microscopic analysis was repeated only on the SF-biofilm samples, and the volunteers were allowed to brush their teeth. Multivariate analyses revealed that the donors had a significant effect on the composition of the biofilm, confirming its subject-dependent character. The bacterial composition of the SF-biofilm was similar to the TF-biofilm, with significant differential abundance detected in very few taxa of low abundance. The toothbrushing during the formation of SF-biofilm was the only factor that conditioned the thickness or bacterial viability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".