Additional file 2 of Dental service utilization and the COVID-19 pandemic, a micro-data analysis
Bibliographic record
Abstract
Additional file 2: Fig. A1. Service Capacity. Fig. A2. Treatments by Clinics. Table A1. Level of Dental Services and the Pandemic (S+P). Table A2. Level of Dental Services and the Pandemic (S). Table A3. Level of Dental Services and the Pandemic (P). Table A4. Level of Dental Services and the Pandemic (S&P). Table A5. Level of Dental Services and the Pandemic (DID test). Table A6. Change in Dental Services and the Pandemic (S+P). Table A7. Change in Dental Services and the Pandemic (S). Table A8. Change in Dental Services and the Pandemic (P). Table A9. Change in Dental Services and the Pandemic (S&P). Table A10. Change in Dental Services and the Pandemic (DID test). Table A11. Level of Relative Dental Services and the Pandemic. Table A12. Level of Relative Dental Services and the Pandemic.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.760 | 0.117 |
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".