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Record W4226191793 · doi:10.1051/e3sconf/202234705014

Development of Regional Climate Model (RCM) for Cameron Highlands based on Representative Concentration Pathways (RCP) 4.5 and 8.5

2022· article· en· W4226191793 on OpenAlexaboutno aff
Jin Min Pang, Kok Weng Tan

Bibliographic record

VenueE3S Web of Conferences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsDownscalingPrecipitationEnvironmental scienceClimatologyMean squared errorReturn periodClimate changeClimate modelFlood mythLongitudeLinear regressionLatitudeMeteorologyAtmospheric sciencesStatisticsMathematicsGeographyGeology

Abstract

fetched live from OpenAlex

Development of local climate is important for climate hazard assessment. Cameron Highlands was chosen as study area (longitude from 101°21’ to 101°30’ and latitude from 4°19’ to 4°37’) for climate downscaling. This paper presents the work of downscaling techniques and regional climate model (RCM) development. The second-generation Canadian Earth System Model (CanESM2) based on the representative concentration pathways (RCP) 4.5 and 8.5 was applied to develop the local climate model for the period 2020-2100. The climatic parameters chosen were temperature (maximum and minimum) and rainfall. The simulated RCMs are then analysed using statistical reliability including Pearson correlation coefficient, linear regression, root mean square error (RMSE) and probability density function (PDF). The result showed that the simulated maximum, minimum temperature, and rainfall are most likely to follow RCP 8.5 scenario. Precipitation threshold for occurrence of flood event was estimated using intensity duration frequency (IDF) relationship generated by maximum precipitation. Return period of two years and four hours rainfall duration is used for threshold estimation as the rainfall is convective. The daily rainfall threshold for flood occurrence is estimated to be 11.3 mm/hr.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.439

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 integrity0.0000.000
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 head0.067
GPT teacher head0.274
Teacher spread0.207 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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