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
A number of metaphysical algorithms have been developed in recent years. Most of these algorithms are inspired by physical processes or living beings' behaviour. In this paper, a new algorithm namely "Hide Objects Game Optimization (HOGO)" is presented to obtain quasi-optimal solution. It is inspired by an old game and the searcher agents who try to find a hidden object in a given space. In this game, any player must notice the following points: (a) pay attention to the voices made by the coach for players, (b) get closer to the best player for whom the coach made the loudest voice, (c) take influence from the voices made by the coach for other players, (d) compare the new voice after a move with the old voice before the move and return back in case the voice gets lower. HOGO is tested on 23 well-known benchmark test functions and is compared with eight optimization algorithms: Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, Teaching Learning Based Optimization, Grey Wolf Optimizer, Grasshopper Optimization Algorithm, Spotted Hyena Optimizer, and Emperor Penguin Optimizer. The results and data obtained from applying HOGO and other said algorithms show that HOGO is able to provide better results in comparison with other well-known optimization algorithms.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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