Corrigendum to “Effect modification of greenness on the association between heat and mortality: A multi-city multi-country study”
Bibliographic record
Abstract
The authors would like to acknowledge the participation of 4 additional authors to this article. They contributed to the processing of the Japanese dataset used in the study. The corrected authorship, the corrected Acknowledgements and the corrected Contributors and Declaration of interests sections are presented below. H.M.C. and W.L. performed data analysis. W.L., D.R., A.U., A.E., A.M.V., A.Z., A.G., A.Z., A.T., B.A., B.F., C.Í., C.Å., C.F.S.N., E.I., E.L., F.M., F.A., F.S., H.O. H.K., J.K., J.M., J.S., J.J., K.K., M.H.D., M.S.R., M.H., M.P., N.R., N.S., S.O., S.T., X.S., Y.H., Y.K., YL.G., Y.G., and M.L.B. provided essential data resources. H.M.C. drafted the first version. H.M.C. and M.L.B. performed writing and editing the manuscript. H.M.C., W.L., A.U., A.G., A.T., B.A., E.L., F.S., S.T., and M.L.B. conducted reviewing. H.M.C. and D.R. have developed the figures. All authors have read and acknowledged the final manuscript.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".