Bibliographic record
Abstract
actual total loss 278-81 Afghanistan 222-4 Africa 189-91 see also individual regions and countries African Pride theft 78 African Union (AU) 97, 99, 189 Agha, Osman 264 Albania 130 Alondra Rainbow hijacking 300 Amaechi, Chibuike Rotimi 75 Amnesty International 63-4, 112 Anambas Islands, Indonesia 24, 29 The Andreas Lemos case (1982) 301 Annual Report of the International Maritime Bureau (IMB) 16, 22 armed conflict collateral damage in 174 use of force, distinguished from piracy 142-3, 156-7, 180 armed force, use of term 156 armed robbery against ships differing definitions of 1, 14 IMO provisions 14, 226-8, 233 sovereignty and 14-15 UNCLOS provisions 142-5 UNSC resolutions 124-5 see also Regional Cooperation Agreement on Combating Piracy and Armed Robbery against Ships in Asia (ReCAAP); individual incidents and cases Army War College (US) 212-13 arrest and detention 108, 130, 183, 192-3 Arusha, Tanzania 141 Asian Economic Crisis (1997) 15-16
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.641 | 0.514 |
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".