Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1761.vid This paper offers an improvement over previous load balancing research. Previous research proposed a novel algorithm, allowing a network of nodes that track and report entities to rapidly distribute that responsibility with no dedicated command and control messaging or thrashing of responsibility. The assumption and limitation of the previous research is the algorithm assumed a predefined balance point for the network. The predefined balance point could be an equal load by all participants or some other distribution defined as pre-mission data. New research provides a method for a network to balance around the immediate needs of the network, constrained only by the capacity that each participant is available to help at that time while maintaining the favorable convergence and scaling properties of load balancing. This value can be limited by the available sensor timelines or constrained by the node operator. This approach is particularly of interest to networks with a symmetric information exchange scheme where the signal to noise ratio (and hence bandwidth) can change between participants due to range or jamming conditions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it