Bibliographic record
Abstract
The future of military action will increasingly require new methods based on 'swarming' tactics in which a multitude of small units or 'pods' can operate in clusters with an overlaying network transmitting information. This project aims at developing concise spatio-temporal models of the large scale dynamics of swarm. The focus is on 'fluid-like' swarms in which the individual units have fairly distributed but localized density. The models have some connection to classical problems in fluid dynamics, with a potential for a richer structure arising from cooperativity between units in the swarm and from self-propulsion of individual units. The justification of the models is based on the internal dynamics swarming as opposed to classical principles of physical fluid flow. Models will be tested against numerical particle-based (Lagrangian) simulations and will be compared with known behavior from biological swarms such as locusts, ants, and fish. This biology-based portion of this research project will include collaboration with Mark Lewis, the Canada Research Chair of Mathematical Biology at the Univ. of Alberta. The second part of this program involves bio-engineering motivated' design of swarm". We consider the inverse problem: given a large scale dynamics for a swarm, how can one design individual motion to achieve this outcome? Our approach is to start with continuum models designed to have desired solutions. We will use knowledge gained from the biological models to derive swarming algorithms that could have both military and industrial use. The designed swarms will include and additional component not present in biological models, that of a communications network distributed among the swarmer subgroups that will facilitate operations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".