Outreach of climate change attribution in Hungary using seasonal indicators
Bibliographic record
Abstract
<!--!introduction!--> 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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".