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
Human beings require nourishment for the body, mind, and soul. To nourish tomorrow demands sustainable, clean and healthy food, water, air, healthcare, energy, living quarters, communities, and governance for everyone. This volume brings together twenty-four experts — comprising engineers, scientists, economists, architects, academics, and public servants from around the world — to share their views on how we could sustainably nourish people and the planet. In this book, the theme of building environments in which life — human and non-human — can co-exist, grow, and thrive in, is explored from multiple aspects. From agriculture and food security to drinking water, energy generation, energy storage, waste management and treatment, to building for and encouraging biodiversity in marinas, to establishing resilient communities that can recover quickly from both manmade and natural disasters. This book is a valuable resource for readers in the fields of biological science, agriculture, and sustainability. It is also a thought-provoking volume for those who simply want to know more about the complex issue of nourishing the world.
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 | 0.002 | 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 | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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