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Record W2792397498 · doi:10.1071/rj17100

How do herders do well? Profitability potential of livestock grazing in Inner Mongolia, China, across ecosystem types

2018· article· en· W2792397498 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Rangeland Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsLivestockStockingGrassland degradationGrazingRangelandChinaGrasslandGeographyInner mongoliaProfit (economics)Profitability indexEcosystemPastoralismPastureAgroforestryAgricultural economicsSocioeconomicsBusinessForestryEcologyEnvironmental scienceEconomicsBiology

Abstract

fetched live from OpenAlex

Livestock production has increased in Inner Mongolia, China, despite widespread documentation of grassland degradation. To begin investigating the relationship that produces these trends, we studied farm-level decisions of herder households. We estimated economic enterprise budgets for 15 counties in Inner Mongolia across five ecosystems in 2009 and 2014 by using household survey data. Six counties decreased livestock stocking rates and had improved profit over time. The remaining counties increased their stocking rates over the period studied and profit decreased for all but one county. Livestock operators who reported negative profit over the 5 years were located across ecosystem types and reported a large number of weather shocks that affected grassland availability. Removing the opportunity cost of land and labour from the economic enterprise budgets resulted in a positive profit for all counties, which may explain why herders continue to increase stocking rates with decreased grassland availability over time.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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