Synthesising of reactionless flexible mechanisms for space applications
Classification
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".
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
Often, one achieves the dynamic balancing condition by resorting to counter-devices approach, however, by doing this, one adds extra weight and therefore the inertia are increased inside the whole system, which is not cost-effective when the system is sent into space and later used in space. In this study, it is suggested one is able to achieve the reactionless condition through combining the self-balanced system. For example, the dynamic balancing condition can be realised via the reconfiguration concept. Extra counter-mass is not employed but through reconfiguring the whole structure, in this way, the system will not get to be heavy and therefore, reduce the energy costs and make the system more applicable and flexible for space applications. Based on this concept, first and foremost, one needs to balance a single component through the reconfiguration approach (i.e., decomposition process) and after that integrate the above balanced components to build the entire system (i.e., integration process). Finally, with the mechanical reconfiguration, the control laws governing the operation of the mechanism also need to be changed, so as to make whole systems more flexible when they are used in space.
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 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 | 0.000 |
| 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) | 0.000 | 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