Paddy Production and Climate Change Variation in Selangor, Malaysia
Bibliographic record
Abstract
The North West Selangor Integrated Agriculture Development Agency (NWS-IADA) has the most productive agricultural land in Selangor. This is partly because of the inherent high fertility of the soils, and the moderate variable climate. However, with the increasing global concern about climate impacts, there is a need to examine the issue and this article presents a study that examined the relative importance of climate influences on the paddy production rate over 28 years (1980-2008). Data collection involved compiling and analyzing climate records from MARDI Tg. Karang auxiliary station (Station no. 44325, 24m m.s.l) at the coordinates of N 03° 27’ 17” E 101° 09’ 24”. The results indicate that the average rainfall recorded was 1, 765 mm which is similar to the national rainfall trend. Meanwhile, the daily humidity varied between 94 – 96% (8.00 AM) and around 70% (2.00 PM) while the sunshine hours ranged between 2.3 to 9.5 hours. A correlation analysis between the production yield and climatic data at the studied area for the year 2000 – 2008 showed that for precipitation, rainfall is redundant during the main season while during the off season it bears direct effect on the production yield with R2 value of -0.293 and 0.1715, respectively. Sunshine hours and temperature demonstrate their importance to production yield as suggested by their respective R2 values.
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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 | 0.001 | 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) | 0.000 | 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".