The effects of heatwaves days on the birth rate in the capital city of Serbia
Bibliographic record
Abstract
The aim of the paper is to examine the effects of hot days on the birth rate in the Belgrade Region. The facts that even a quarter of the population and live births are concentrated in the capital and that Belgrade stands out as a heat island with an above average air temperature make this analysis fully justified and significant. The authors use data from the Demographic Statistics on almost 108.000 live births in the capital in the period 2015-2020, as well as data from the Digital Atlas of Serbia on daily average temperatures in the same period. Our approach, based on regression analysis, allows examining the effects of random variations in the distribution of daily air temperatures (the focus is on days with an average temperature above 26.6°C) on the birth rate up to 10 months after exposure (to high air temperatures). The main results indicate a strong negative eff ECT of hot days on the birth rate 9 months after exposure to high temperatures. Such findings help explain the observed decline in birth rates during the spring months, and as such may be useful in formulating strategies to mitigate the effects of hot temperature shocks to which the population of Belgrade will be increasingly exposed in the future. The questions that arise for future research are the following: can we talk about a birth compensation period after heat waves, and will climate change increasingly move births to the summer months, bearing in mind that the population's exposure to high temperatures in the third quarter of the year is increasing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".