Bibliographic record
Abstract
There is a flood of modern machines in the world, whose form has been taken by AI, it is entering the life of every person, whether it is your TV or fridge, it is visible in every field, from automobile to mobiles phone. Everyone has become accustomed to AI. The special thing is that in this era of modernity, the help of AI is being taken to increase the yield of agriculture. Farmers are able to spray their crops through drones. So in the same world, AI is also being used in large quantities in the fields of war like drone missile, guided missile, satellite missile etc. This AI is moving towards the new future of the world by which either peace will be established in the future or a devastating war will be fought. Although AI is likely to be operated by humans only because if this does not happen then the future of the world will be in the hands of AI. But given the various AI stages, this can only be imagined The Russia-Ukraine war is now at a more disastrous stage, in such a situation, what role AI can play in the search for peace remains to be seen. Due to differences with you and hunger for expansion, today the world is once again at a disastrous stage, among them China - Taiwan, Isreal-Iran etc. countries are standing on the front-line regarding the war against each other. Which cannot be seen normally. The related research paper attracts more attention to this and also questions the utility of AI. Although it will prove useful in some areas, can Ai be used for peace instead of war? it remains to be known
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 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".