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Record W4404475660 · doi:10.31315/imagi.v4i2.13765

Dampak Emisi PLTU Suralaya terhadap Konsentrasi PM2.5 di Jakarta Raya: Analisis Spasio-Temporal Berbasis Citra Satelit MODIS (2019-2022)

2024· article· id· W4404475660 on OpenAlex
Lalu Teguh Purnama Ramdhanu, Shada Salsabila, Lalu Muhamad Jaelani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Ilmiah Geomatika · 2024
Typearticle
Languageid
FieldEngineering
TopicWetland Management and Conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Salah satu polutan udara yang menjadi perhatian utama adalah Particulate Matter 2.5 (PM2.5), yang salah satunya bersumber dari pembakaran bahan bakar fosil, seperti pembangkit listrik tenaga uap (PLTU).Penelitian ini mengkaji dampak emisi PLTU Suralaya, yang berlokasi di Banten, terhadap konsentrasi PM2.5 di Jakarta Raya. Lokasi PLTU yang relatif dekat dengan Jakarta Raya menjadi dasar pemilihan objek studi ini. Penelitian ini memanfaatkan data citra satelit karena keunggulannya dalam hal cakupan wilayah, frekuensi pengamatan, dan efisiensi biaya operasional. Data satelit yang digunakan adalah Moderate Resolution Imaging Spectroradiometer (MODIS) untuk menganalisis konsentrasi PM2.5 yang diperoleh dari Aerosol Optical Depth (AOD) melalui konversi menggunakan algoritma R. Li dkk. Hasil analisis menunjukkan bahwa tidak terdapat hubungan yang signifikan antara arah angin dengan konsentrasi PM2.5 di Jakarta Raya. Berdasarkan temuan ini, dapat disimpulkan bahwa PLTU Suralaya tidak terbukti secara signifikan mempengaruhi konsentrasi PM2.5 di Jakarta Raya.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.222
Teacher spread0.210 · 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