A proposed model for small-world structural organization of mission teams and tasks in order to optimize efficiency and minimize costs
Bibliographic record
Abstract
Abstract We propose a model of structural organization and intercommunication between all elements of every team involved in the development of a space probe to improve efficiency. Such structure is built to minimize path between any two elements, allowing fast information flow in the structure. Structures are usually very clustered inside each task team but only the heads of departments, or occasional meetings, usually assure the links between team elements. This is responsible for a lack of information exchange between staff members of each team. We propose the establishment of permanent small working groups of staff elements from different teams, in a random but permanent basis. The elements chosen for such connections establishment can be chosen on a temporary basis, but the connections must exist permanently because only with permanent connections can information flow when needed. A few of such random connections between staff members will diminish the average path length, between any two elements of any team, for information exchange. A small world structure will emerge with low internal energy costs, which is the structure used by biological neuronal systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".