Rim of the Pacific (RIMPAC). Programmatic Environmental Assessment
Bibliographic record
Abstract
RIMPAC is a multinational, sea control/power projection fleet exercise that has been performed biennially for the last 30 years. The purpose of RIMPAC is to implement a selected set of exercises that is combined into a sea control/power projection fleet training exercise in a multi-threat environment. RIMPAC exercises also demonstrate the ability of a multinational force to communicate and operate in simulated hostile scenarios. RIMPAC 2002 will be the eighteenth in a series involving forces from Australia, Canada and the United States; the twelfth involving the Japanese Maritime Self Defense Force; the seventh involving the Republic of Korea Navy; and the fourth involving the Chilean Navy. The United Kingdom, France and Peru have been accepted to participate in RIMPAC 2002. RIMPAC 2002 is scheduled to be conducted from 25 June to 23 July 2002. During initial planning meetings in July 2001, the Action proponent, Commander, THIRD Fleet, gathered input from possible participants to understand the various testing and training needs. Operations personnel developed a general scenario to accommodate testing and training needs. As a result of three planning conferences considering budget and time constraints, as well as safety and environmental considerations, a final scenario and set of exercises were developed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".