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
Last year was one of broken records and broken promises. We saw new highs of greenhouse gas emissions, temperature records tumbling and climate impacts arriving stronger and faster. The finance to help vulnerable communities adapt to climate change isn’t being delivered. At the same time, most of the Sustainable Development Goals (SDGs) are off track at the halfway point of the 2030 Agenda for Sustainable Development. There are many reasons for this, but it is clear that slow action on the triple planetary crisis of climate change, nature and biodiversity loss, and pollution and waste is a major driving force. This is the downside. The upside is that the global response to the triple planetary crisis intensified. Efforts to combat pollution and waste received a shot in the arm with the agreement of the Global Framework on Chemicals and progress on the global instrument on plastic pollution, which should be ready by 2024. Nations adopted a treaty to protect biodiversity in the ocean beyond national borders, while key guidelines to help the private sector reduce its impact on nature were released – a boost to the Kunming-Montreal Global Biodiversity Framework, the implementation of which gathered pace. Finally, the United Nations Climate Change Conference, COP28, delivered a clear call on countries to transition away from fossil fuels – alongside a framework on the Global Goal on Adaptation, operationalizing the Loss and Damage Fund, and new commitments on sustainable cooling, methane reduction, tripling renewable energy targets and nature breakthroughs. The UN Environment Programme (UNEP) played an important role in many of these processes – by providing key science and solutions on the triple planetary crisis, convening and supporting important negotiations, hosting critical multilateral environmental agreements, working with the private and financial sectors to align funding with global processes and supporting Member States to deliver on their commitments.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it