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Record W1947498638

Grazing in arid North America: A biogeographical approach

2006· article· fr· W1947498638 on OpenAlexaboutno aff
Lynn Huntsinger, Paul F. Starrs

Bibliographic record

VenueScience et changements planétaires / Sécheresse · 2006
Typearticle
Languagefr
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsRangelandLivestockGeographyGrazingAgroforestryAridSubsistence agricultureArable landPastoralismEcologyForestryEnvironmental scienceBiologyAgriculture
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.241
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2006
Admission routes1
Has abstractyes

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