projected changes in temperature and precipitation in Sarawak state of Malaysia for selected CMIP5 climate scenarios
Bibliographic record
Abstract
This article explores the projected changes in precipitation, maximum temperature (T max ) and minimum temperature (T min ) in the Malaysian state of Sarawak under Representative Concentration Pathways (RCPs) with the CanESM2 Global Circulation Model.The Statistical Downscaling Model (SDSM) was used to downscale these climate variables at three stations in Sarawak.The model performed well during the validation period and thus was used for future projections under three RCPs with the CanESM2 General Circulation Model.It is noted that the T max will increase by 1.94°C at Kuching, 0.09°C at Bintulu and 1.29°C at Limbang, when comparing the period of 2071-2100 with the baseline period of 1981-2010, under the most robust scenario of RCP8.5.T min is also expected to increase by 1.21°C at Kuching, 0.15°C at Bintulu and 2.08°C at Limbang, under the RCP 8.5 projection for the same period.The precipitation at Kuching and Bintulu is expected to increase slightly to 1.6% and 1.4% at Kuching and Bintulu respectively; however, the seasonal shift is projected as follows: lesser precipitation during the December-February period and more during the June-August season.On the other hand, precipitation is expected to increase at Limbang during all seasons, when compared with the period of 1981-2010; it is expected that under RCP4.5 the annual precipitation at Limbang will increase by 10.5% during the 2071-2100 period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".