Grazing in arid North America: A biogeographical approach
Bibliographic record
Abstract
More than one-third of the North American continent, about one billion hectares of the United States, Canada, and Mexico, can be considered arid and semiarid. Since the sixteenth century livestock grazing has been the dominant use of non-arable, “marginal” lands, but in recent decades competition from other uses, and large-scale production of cereal and forage crops, has had major impacts on forms of rangeland production. Poverty and subsistence ranching influence rangeland use and condition in Mexico more than in the other countries. Approximately 28 million beef cows, 4 million ewes, and 5 million goats graze arid North America, with livestock densities increasing from north to south. A transect running west to east across the western continent illustrates the geographical diversity of resources, landownerships, and land uses. The major rangeland types and livestock production characteristics for eight ecological regions are described. In the United States, growing demand for goat meat has stimulated an increase in production, while sheep numbers steadily decline due to consumer preferences and vulnerability to predators and dogs. Interest in the use of goats and other livestock for vegetation management, and in “natural” meats, may influence future livestock grazing patterns on rangelands.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".