Design of weight-optimized space frame for the Canadian large adaptive reflector
Bibliographic record
Abstract
The Square-Kilometer Array (SKA) is an international project for building the next radio telescope. With a collecting area of one square-kilometer, the SKA will be 100 times more sensitive than current radio telescopes. The concepts for the SKA elements include nested phased arrays, large spherical reflectors, and many small parabolic antennas. The Large Adaptive Reflector (LAR) is the Canadian concept of building the SKA. The LAR is a long focal-length parabolic reflector which uses an airborne platform to support the focal receiver. The feed is held in plane by a tension-structure consisting of three or more tethers tensioned by the lift of a large helium-filled aerostat. The reflector is made up of segmented panels whose height and angle to zenith of segmented panels can be adjusted to focus on any point within zenith and azimuth angle coverage. Unlike conventional radio telescope, LAR is based on reflective optics thus the usable frequency range is limited by the surface accuracy of the reflector. Main structural components of the LAR are foundations, actuators (primary and secondary), main support structures, and reflector panels. This report includes investigation on the feasibility of using LAR antennas as elements to form the SKA, and the conceptual design of a triangular space frame, which is used as the main support structure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".