Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: 2022 assessment report
Bibliographic record
Abstract
“The Assessment for the four year period from 2018-2022 was published on the 4th May 2023 on the UNEP Ozone Secretariat website (https://ozone.unep.org/environmental-effects-stratospheric-ozone-depletion-uv-radiation-and-interactions-climate-change). The report involves a comprehensive assessment of the interactive effects of climate change, ozone depletion and UV radiation in eight key areas of global importance. These are also synthesized in an Executive Summary and will be accompanied by a Q&A for the general public. The assessment report is written in plain language to be understood by politicians and interested parties. For the first time this year, the report includes additional sections on COVID-19 and on microplastics in the environment. I am Co-Lead Author of the section that explains the state-of-the-art related to our knowledge of global effects on terrestrial ecosystems and biogeochemical cycling. There, we considered the potential detrimental effects on crop production and biodiversity that would have occurred in the “world avoided” by of our successful implementation of the Montreal Protocol and its Kigali Amendment. Compliance with the HFC restrictions in this amendment saves a projected 0.5 C of climate warming during the 21st Century. This sets the benchmark as a globally effective and successful climate treaty.” - Matthew Robson, Senior Lecturer in Forestry at the University of Cumbria, UK.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".