Additional file 5 of Sex difference of pre- and post-natal exposure to six developmental neurotoxicants on intellectual abilities: a systematic review and meta-analysis of human studies
Bibliographic record
Abstract
Additional file 5: Sensitivity Analyses Figure 1. Funnel Plot - General Intelligence in Males. Figure 2. Funnel Plot - General Intelligence in Females. Figure 3. Funnel Plot - Nonverbal Intelligence in Males. Figure 4. Funnel Plot - Nonverbal Intelligence in Females. Figure 5. Funnel Plot - Verbal Intelligence in Males. Figure 6. Funnel Plot - Verbal Intelligence in Females. Figure 7. Funnel Plot - Postnatal Lead and General Intelligence in Males. Figure 8. Funnel Plot - Postnatal Lead and General Intelligence in Females. Figure 9. Leave One Out - General Intelligence in Males. Figure 10. Leave One Out - General Intelligence in Females. Figure 11. Leave One Out - Nonverbal Intelligence in Males. Figure 12. Leave One Out - Nonverbal Intelligence in Females. Figure 13. Leave One Out - Verbal Intelligence in Males. Figure 14. Leave One Out - Verbal Intelligence in Females. Figure 15. Leave One Out - Postnatal Lead and General Intelligence in Males. Figure 16. Leave One Out - Postnatal Lead and General Intelligence in Females. Figure 17. Low Risk of Bias - General Intelligence in Males. Figure 18. Low Risk of Bias - General Intelligence in Females. Figure 19. Low Risk of Bias - Nonverbal Intelligence in Males. Figure 20. Low Risk of Bias - Nonverbal Intelligence in Females. Figure 21. Low Risk of Bias - Verbal Intelligence in Males. Figure 22. Low Risk of Bias - Verbal Intelligence in Females.
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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.661 | 0.018 |
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".