A practical application of FGDLS to birds flock trajectory
Bibliographic record
Abstract
Abstract:- In this paper we describe a practical application of the Feedback Guided Dynamic Loop Scheduling (FGDLS) algorithm. FGDLS is a recent scheduling method that was proposed in Bull [1] to deal with a sequence of similar or identical parallel loops. The presumption is that the parallel loops are very similar with the same number of iterations and with iterations that do not vary much from one step to another. The FGDLS algorithm uses feedback information from the previous parallel iteration e.g. measured execution times to schedule the current parallel loop. So far all the applications of FGDLS have considered only identical iterations within the parallel loops. In this article we will propose a practical application to simulate the flocking birds (boids) trajectory. For this application the iterations are not the same varying slightly from one parallel loop to another. Key-Words: Dynamic scheduling, boid algorithm, visualization. 1
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".