Pemetaan Distribusi Petir Untuk Wilayah Manado Tahun 2013 Dan 2014
Bibliographic record
Abstract
Telah dilakukan penelitian untuk memetakan distribusi petir untuk wilayah Manado berdasarkan data petir tahun 2013 dan 2014. Data real time sambaran petir dari rekaman lightning detector diolah menggunakan beberapa program, yaitu Lightning 2000, Golden Software Surfer 8, Lightning Data Processing, GIS 10.3, Google Earth dan Microsoft Excel. Pada program GIS 10.3 data yang didapatkan kemudian dipetakan menggunakan metode Kriging. Hasil yang diperoleh dalam penelitian ini berupa peta kontur distribusi petir di wilayah Kota Manado. Berdasarkan hasil dari pengolahan data, diperoleh data yang menunjukkan bahwa kejadian petir tertinggi terdapat pada bulan Oktober 2013 yaitu sebanyak 6.540 kejadian dan bulan Mei 2014 yaitu sebanyak 7.330 kejadian petir. Distribusi petir CG+ tertinggi terdapat pada kecamatan Wenang dan tidak ada kejadian petir CG+ di 4 kecamatan yaitu Kecamatan Tikala, Paal Dua, Singkil dan TumintingResearch has been done to make a distribution map for Manado area based on lightning data of year 2013 and 2014. The real time data of lightning strikes from lightning detector processed by using a few program that is Lightning 2000, Golden Software Surfer 8, Lightning Data Processing, GIS 10.3, Google Earth and Microsoft Excel. Data that we got from GIS 10.3 use for mapping with Kriging method. Output from this research is contour map in Manado city area. Based on output from processed data, we got data that the highest lightning event happened in October 2013 that is 6.540 event and in May 2014 that is 7.330 lightning event. Highest CG+ lightning distribution located in Wenang Districts and there is no CG+ lightning event in 4 districts which is Tikala, Paal Dua, Singkil and Tuminting Districts
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".