Bibliographic record
Abstract
To Our Readers 2013 Statewide Conference UC ANR: California Roots, Global Reach I n 2025, the population of the Earth is projected to be over 8 billion. How can we sustainably feed a population of that size? On April 9, 2013, the University of California, Division of Ag- riculture and Natural Resources (ANR), will host a Global Food Systems Forum on the challenges faced by food producers and suppliers in a world of growing population, strains on natural ecosystems, “We need a new sustainable paradigm for development based on rights and equity — the one we have has proven itself unsustainable.” Mary Robinson, former president of Ireland keynote speaker, UC ANR Statewide Conference 2013 add more land or more water. But we’re not going to do much of either.” UC will continue to play a leadership role in convening these kinds of dialogues, which will help guide the preparation of the next generation of students for careers in sustainable food production, as well as focus innovative research on finding so- lutions to these worldwide challenges. As someone interested in California and global agriculture, please join us in this important dis- cussion. You can follow us on twitter @ucanr #Food2025 for updates, and more information on the forum will be available on our website at http://ucanr.edu. Barbara Allen-Diaz Vice President UC Division of Agriculture and Natural Resources shifting geopolitics and other converging forces. The conversation will take place in Ontario, Cali- fornia, as part of a 3-day ANR statewide conference with the theme “California Roots, Global Reach.” The forum will include a keynote address by Mary Robinson, former President of Ireland and founder of the Mary Robinson Foundation — Climate Justice, an organization dedicated to a human-centered approach to development and eq- uitable stewardship of the Earth’s resources. The program will consist of three panels, two with a global focus and the third with a California emphasis. The first panel will address the geopo- litical, ethical, economic and technical challenges facing food systems worldwide. Panel two will dis- cuss whether we can meet these challenges without depleting natural resources, and the third panel will question the implications, responsibilities and opportunities from a California perspective. The panels will offer a lively interchange of ideas mod- erated by two award-wining authors and journal- ists, Michael Specter and Mark Arax. As William Lesher, former chief economist for the USDA, recently stated: “We’re going to have to produce more food in the next 40 years than we have in the last 10,000. Some people say we’ll just http://californiaagriculture.ucanr.edu • January–MArch 2013 5
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.574 | 0.291 |
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".