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Record W2326533133 · doi:10.5539/sar.v5n2p103

Sustainable Grassland Management: An Exploratory Study of Progressive Ranchers in Nebraska

2016· article· en· W2326533133 on OpenAlexvenueno aff
Stephanie M. Kennedy, Mark E. Burbach, Maggi Sliwinski

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGrasslandLivelihoodStewardship (theology)BusinessEnvironmental resource managementAgroforestryEcosystem servicesProductivityEnvironmental planningNatural resource economicsEcosystemGeographyEcologyEnvironmental scienceAgricultureEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Well-managed grasslands provide numerous ecosystem services. Ranchers who employ sustainable grazing practices limit grassland conversion and conserve critical habitats. This phenomenological study explored the grassland management decisions of progressive ranchers in Nebraska. Each individual interviewed for this study is proactive about the state of their grasslands, whether they are motivated by financial or conservation factors. Throughout the evolution of their businesses, these ranchers have taken steps to improve their management techniques and continue to employ new strategies while planning for the long-term productivity of their grasslands. For policy makers and educators seeking to improve grassland management decisions, demonstrating new methods to be economical, promoting stewardship, and allowing for flexible implementation may increase acceptance of recommendations. Because progressive ranchers’ livelihoods are connected to the land, and they are long-term goal oriented, they closely scrutinize, yet are open to advancing grassland management practices that benefit their cash flow, their pastures, their animals, and their families.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.304
Teacher spread0.287 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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