Bibliographic record
Abstract
The application of the armed UAV has been questioned for years. This paper hopes to examine the ethical rationality of using the armed UAV. Before this, we question some blames from the Pacifists, especially the blaming on using UAV in the military attack on terrorists. Ethical questions about the UAV’ military actions mainly concentrate in two aspects: the right to life and ethics of science and technology. The former involves the subjects' value sequencing and moral selection problem, which requires discussions under specific situations, otherwise it will make no sense. As for the latter, ethics of technology, defects would be resolved in the development. It’s important to not get technological risk and scientific ethics confused, which would make discussions on a wrong way. ions and laws related to deep seabed mining to mitigate its effect to the marine environment coinciding to the requirements of these conventions. The purpose of this study is to explore the preparedness of Malaysia to embark on exploration of deep seabed mining in areas beyond the national jurisdiction while observing the effects of deep seabed mining to the marine environment. The challenges in exploring the deep seabed mining as well as the relevant international and national laws related to deep seabed mining will also be observed in this study.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".