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
Major floods and droughts have prevailed in many countries throughout 2015.South Africa saw the emergence of its worst drought in 30 years, Ethiopia is threatened with a major food crisis, and California suffered its fourth consecutive year of drought.Floods caused over 2 000 deaths in India last summer, while England, Paraguay and South Carolina reported unprecedented flood damage.The trouble is, climate change is expected to increase the frequency and severity of such extreme weather events in the coming years.Governments can do a lot better in improving the mitigation and management of drought and flood risks, argues Mitigating Droughts and Floods in Agriculture: Policy Lessons and Approaches.Part of broader OECD work on risk management in agriculture, this report builds on recent trends, experiences and research in OECD countries, particularly Australia, Canada, France, Spain and the UK, in the sustainable management of floods and droughts in agriculture, and sets out 1 www.oecdobserver.org
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it