The XVII International Grassland Congress and Challenges for the Future of Tropical Grasslands: A Developing Country Perspective
Bibliographic record
Abstract
All too soon we are to draw down the curtains on the XVIII International Grassland Congress and it is my pleasure and honour to contribute to the closing statements of this historic event, the first of its kind in Canada and one to bring us to the end of the present millennium as we as set the stage for the next. I do this on behalf of my colleagues in Africa, Asia and South America. The thirty themes and the nearly 2000 technical presentations underscore the growing complexity of the role of our grasslands in shaping the general well-being of mankind. The XIX Congress in Brazil in 2001 will no doubt shift the frontiers of knowledge even further in the quest for sustainable use of our grassland resources. Mr. Chairman, I make my address from a developing country perspective because the bulk of tropical pastures occurs in this region characterized by how per capita income (less than US $500), a complex agro-ecology from humid to arid grasslands, very fragile grasslands and, especially for Africa, a general food deficit, an increasing pressure on the grasslands and a generally poor awareness of the need to invest in the grasslands as a sustainable commodity for livestock agriculture within the context of a traditional grassland based ruminant livestock production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".