66 Randel Lecture: Ensuring a Leading Role for USA Animal Protein in Our World’s Food Future
Bibliographic record
Abstract
Abstract Global population is expected to increase by 2.5 billion people by 2072 according to UN projections made recently. Africa will lead growth by adding.8 billion people to its continent (147% increase), and 45 of 50 global countries with greatest percentage growth will be in Africa. Europe’s population will decline by an estimated 100 million people (13% decline), Asia’s population will increase by 12%, and North and South America will increase about 20%. North and South America have 3- to 4-fold more cropland per capita than Africa, but East and South Asia have about one half as much as Africa. Coincidental with this growth in population, changes in climate will reduce yields of food crops in equatorial regions and in regions with shortages of water. Simultaneously, climate change will create an estimated 1.5 billion hectares of usable cropland that currently does not have a growing season sufficiently long to produce a crop, virtually all of this will be in northern Canada and Russia. Diets that include animal products feed more people than vegan-only diets, because essential amino acids and limiting nutrients are provided more efficiently through animal products. Among various diets, dairy and vegetable, egg and vegetable, and omnivore and vegetable diets can feed more people than vegan diets. Animal products must be produced efficiently and have limited detrimental impacts on environments and ecosystems. Currently, GHGs per unit of animal food are 7-fold greater in Africa than in North America and Europe, because yields per animal and output per hectare are up to 50-fold greater in Europe and North America. Two primary goals for the next 50 years are to improve outputs per animal and per hectare in poorly productive regions, and to produce affordable and acceptable animal products that can be exported to these regions from regions that have more land and produce products with low environmental impacts. A major challenge in improving productivity in African countries will be to alter cultural practices that are linked to inefficient production of animal products. Overeating is an issue in many countries, and it will be important for people in these regions to alter diets to reduce obesity and over-consumption of calories and protein. Historically, diets change over time, and during the last century there has been a significant increase in intake of plant-based products in the USA and other countries. Unfortunately, the primary plant-based products that make up more of our diet today are oils and sweeteners, both of which contribute to increased obesity. Thus, simply eating more plant-based products does not guarantee a healthier diet.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.025 |
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".