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Record W2323051121 · doi:10.1061/40976(316)375

Regional Modeling of Climate Change Impact on Peninsular Malaysia Water Resources Jamalluddin bin Shaaban, Ahmad

2008· article· en· W2323051121 on OpenAlexaboutno aff
A. J. Shaaban, Z. Q. Chen, N. Ohara, Mohd Zaki Mat Amin

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2008 · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeStreamflowWater resourcesEnvironmental scienceClimatologyClimate modelPeriod (music)Hydrology (agriculture)GeographyDrainage basinGeology

Abstract

fetched live from OpenAlex

The future projections of climate change by means of Global Climate Models (GCMs) of the Earth provide fundamental coarse-grid-resolution hydroclimate data for studies of the impact of climate change on water resources. We report on a study where the climate change simulations of Coupled Global Climate Model of the Canadian Center for Climate Modeling and Analysis were downscaled by a Regional Hydroclimate Model of Peninsular Malaysia to the scale of the subregions and watersheds of Peninsular Malaysia in order to assess the impact of future climate change on its water resources. Based on simulations of hydroclimatic conditions during the 1984–1993 historical and 2025–2034, 2041–2050 future periods it is concluded that the overall mean monthly streamflow is about the same during the future period and during the historical period for most of the watersheds except Kelantan and Pahang. In those two watersheds there is significant increase in the overall mean monthly streamflow during the future period. It is also clear that the high flow conditions will be magnified in Kelantan, Terengganu, Pahang and Perak River watersheds during the wet months, while low monthly flows will be significantly lower in Selangor and Klang watersheds during the dry months in the future.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.218
Teacher spread0.192 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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