Rekod Jangka Panjang Kepekatan Metana di Malaysia
Bibliographic record
Abstract
Gas metana (CH4) adalah gas rumah hijau yang menyebabkan perubahan iklim dan pemanasan dunia. Kajian CH4 dijalankan untuk melihat tren pelepasan CH4 di Malaysia dalam satu jangka masa yang panjang (10 tahun) dari tahun 2000 hingga 2009 dan menilai hubungan CH4 dengan ozon permukaan (O3). Data CH4 daripada 19 stesen pemantauan kualiti udara automatik berterusan Jabatan Alam Sekitar (JAS) di Malaysia telah dianalisis menggunakan analisis statistik dan korelasi Pearson. Hasil kajian mendapati nilai bacaan kepekatan purata bulanan CH4 tertinggi dicatatkan di stesen Larkin, Johor Bahru iaitu 2.61±0.54 ppm. Nilai purata kepekatan CH4 berdasarkan data yang direkodkan di semua stesen di Malaysia adalah 2.00 ppm. Taburan kepekatan CH4 yang lebih tinggi didapati tertumpu di kawasan bandar dan kawasan perindustrian di Selangor, Melaka dan Johor. Analisis korelasi bagi menentukan hubungan CH4 dengan bahan pencemar O3 mendapati 15 stesen menunjukkan korelasi positif yang sangat kecil dan lemah (r < 0.20 dan 0.20 < r < 0.40) manakala empat stesen lagi menunjukkan korelasi negatif. Hubungan antara CH4 dengan bahan pencemar O3 bagi kesemua stesen adalah tidak signifikan (r < 0.5, p > 0.05). Stesen Shah Alam didapati menunjukkan korelasi CH4 paling tinggi dengan O3 berbanding stesen lain. Pengetahuan asas berkenaan CH4 dalam udara ambien Malaysia yang ditunjukkan dalam kajian ini boleh digunakan untuk menilai potensi impak CH4 terhadap alam sekitar, perubahan iklim dan kesihatan manusia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".