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The Impact of Climate Change on Rainfall Frequency in Taiwan

2012· article· en· W1963959873 on OpenAlexaboutno aff
Wen-Cheng Huang, Y. C. Chiang, Ruo-Ying Wu, Jyun-Long Lee, Shu‐Hua Lin

Bibliographic record

VenueTerrestrial Atmospheric and Oceanic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationTennessee Wildlife Resources Agency
KeywordsClimate changeClimatologyEnvironmental scienceGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

The purpose of this research is to assess climate change impacts on rainfall frequency in Taiwan. The changes in future precipitation were projected statistically from general circulation model (GCM) outputs. Based on five downscaled GCM outputs [China's FGOALS-g1.0, Japan's CGCM2.3.2, the USA's CM2.0, Canada's CGCM3(T47), and France's CM3] under the SRES A1B scenario, the frequency of the maximum consecutive dry days and maximum 1-, 2-, and 3-day rainfall during 2080 -2099 are evaluated and compared with those in the period of 1980 -1999. The results show that by the end of the 21 st century, the risk of droughts and floods over Taiwan has a tendency to increase. The distribution of water resources in Taiwan will be more uneven, with a noticeable change in the ratio of wet and dry seasons. Due to these climate change impacts, future water conservation work will be a major challenge for governments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.292
Teacher spread0.252 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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