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Record W393363030 · doi:10.2480/agrmet.921

Risk Assessment and Regionalization of Agro-meteorological Hazards in Jilin Province, China

2005· article· en· W393363030 on OpenAlexaff
Jiquan Zhang, Seiji Hayakawa, Daowei Zhou, Hui Zhang

Bibliographic record

VenueJournal of Agricultural Meteorology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsScience North
Fundersnot available
KeywordsChinaMeteorological disastersAgricultureEnvironmental scienceTemperate climateWaterlogging (archaeology)Agricultural productivityNatural hazardGeographySustainable developmentDistribution (mathematics)Global warmingHazardClimate changeEnvironmental protectionMeteorologyEcology

Abstract

fetched live from OpenAlex

Jilin province is one of the major maize-growing regions of China and is also one of the major contributors to the Maize Belt of the world’s Temperate Zone. Agro-meteorological hazards such as drought, waterlogging and cool summer occur with very high frequency and affect grain production and social-economic development in Jilin province. Moreover, both the frequency of these hazards and losses from them are considered to be increasing with global warming. This study presents a methodology for risk analysis and assessment of meteorological hazards to agricultural production in Jilin province, China, based on Geographical Information System (GIS) from the viewpoints of climatology, geography, disaster science, disaster risk analysis, environmental science, and so on. This study can be expected to provide the basis for developing strategies to mitigate agro-meteorological hazards and reducing the losses from them, and adjust the medium and long-term distribution of agricultural activities so as to adapt to environmental changes and to ensure agricultural sustainable development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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
Published2005
Admission routes1
Has abstractyes

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