Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) “reasons for concern”
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
Article 2 of the United Nations Framework Convention on Climate Change [United Nations (1992) http://unfccc.int/resource/docs/convkp/conveng.pdf. Accessed February 9, 2009] commits signatory nations to stabilizing greenhouse gas concentrations in the atmosphere at a level that "would prevent dangerous anthropogenic interference (DAI) with the climate system." In an effort to provide some insight into impacts of climate change that might be considered DAI, authors of the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) identified 5 "reasons for concern" (RFCs). Relationships between various impacts reflected in each RFC and increases in global mean temperature (GMT) were portrayed in what has come to be called the "burning embers diagram." In presenting the "embers" in the TAR, IPCC authors did not assess whether any single RFC was more important than any other; nor did they conclude what level of impacts or what atmospheric concentrations of greenhouse gases would constitute DAI, a value judgment that would be policy prescriptive. Here, we describe revisions of the sensitivities of the RFCs to increases in GMT and a more thorough understanding of the concept of vulnerability that has evolved over the past 8 years. This is based on our expert judgment about new findings in the growing literature since the publication of the TAR in 2001, including literature that was assessed in the IPCC Fourth Assessment Report (AR4), as well as additional research published since AR4. Compared with results reported in the TAR, smaller increases in GMT are now estimated to lead to significant or substantial consequences in the framework of the 5 "reasons for concern."
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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