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Record W4321515978 · doi:10.37801/ajad2010.7.1.3

Climate Change and Asian Agriculture

2010· article· en· W4321515978 on OpenAlexaff
Mark W. Rosegrant, Mandy Ewing, Rowena Valmonte-Santos, Tingiu Zhu, Gary Yohe, Ian Burton

Bibliographic record

VenueAsian Journal of Agriculture and Development · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsGreenhouse gasAgricultureClimate changeFood securityNatural resource economicsEnvironmental scienceGlobal warmingAgricultural productivityLivestockLand use, land-use change and forestryAgroforestryAgricultural economicsBusinessEnvironmental protectionGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

Asian and global agriculture will be under significant pressure to meet the demands of rising populations, using finite and often degraded soil and water resources that are predicted to be further stressed by the impacts of climate change. In addition, agriculture and land use change are prominent sources of global greenhouse gas (GHG) emissions. Fertilizer application, livestock rearing, and land management affect levels of GHG in the atmosphere and the amount of carbon storage and sequestration potential. Therefore, while some impending climatic changes will have negative effects on agricultural production in parts of Asia, and especially on resource-poor farmers, the sector also presents opportunities for emission reductions. Warming across the Asian continent will be unevenly distributed, but will certainly lead to crop yield losses in much of the region and subsequent impacts on prices, trade, and food security—disproportionately affecting poor people. Most projections indicate that agriculture in South, Central, and West Asia will be hardest hit.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2010
Admission routes1
Has abstractyes

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