Tinjauan Yuridis Bukti Ilmiah Penyelesaian Perkara Kebakaran Hutan dalam Persfektif Hukum Lingkungan
Bibliographic record
Abstract
Exploration of the natural world is becoming more and more popular since it is seen as being for human benefit. One illustration of how certain reckless elements inflict damage is forest fires. The Kalimantan and Sumatra forest fires had a significant negative impact on ecosystems, society, and the economy. In this journal, the problem of forest fires will be studied from the perspective of environmental ethics theory, namely Anthropocentrism and Biocentrism, where the two views contradict each other related to exploitation that should not be carried out by humans as kholifatul fil Ard. This journal uses the library method using scientific article journals and theses. From the second paradigm on environmental ethics, this study will produce the right perspective in this era regarding the relationship between humans and nature, so that the perspective of society in general can change and can take advantage of this nature according to its portion. Because in the Qur'an it has been explained that some of the damage that exists on earth comes from humans.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".