Outreach of climate change attribution in Hungary using seasonal indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<!--!introduction!--><b></b> The scientific community is well aware of the anthropogenic global warming and its consequences at regional level. However, the public is still not informed well enough, and may be confused especially due to the overwhelming, often contradictory social media environment. This is why we initiated a project with the aim to present and explain scientific results of regional climate change via a national platform (www.masfelfok.hu) established for climate awareness dissemination towards public, within this framework we also use a broad media platform and a large social media network as well. The message is formed as well-illustrated short studies focusing on various seasonally relevant climate indicators, mainly related to climatic extremes. The scientific background is based on calculations using reliable data: observation-based homogenized fine-resolution gridded data for Hungary (HUCLIM), outputs (i.e. CMIP6 data) from global climate model simulations with natural-only forcing as well as historical forcing (when anthropogenic concentration changes are also taken into account), regional climate model simulation outputs (i.e. EUROCORDEX data) for past decades (beginning from the last quarter of the 20th century) and future decades until the end of 21st century with strong mitigation, lighter mitigation, and non-mitigation scenarios. Studies are published in every 4-6 weeks, and online and traditional media connections are also used to outreach the public.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it