Bibliographic record
Abstract
This paper describes a custom robotic process for semi-autonomous survey and layout of architectural elements for a large-scale renovation project. Specifically, the research presents a custom single-task robotic device accompanied with software and workflow methods for surveying, localizing, and marking the positions of façade anchors along the surface of primary steel members. Enabled by custom robotic locomotion and real-time localization, the presented approach offers high-tolerance installation in a low-tolerance environment while minimizing dangerous erection steps that would typically be done by field personnel. The robotic system and the workflow are designed, developed, and tailored to the specific project needs and parameters of the renovated building. For instance, the Halbach magnetic locomotion system presented in this paper is custom designed to traverse the radius of steel pipes that the building structure consists of. On the one hand, such specificity renders the robotic hardware obsolete when applied beyond this project. However, the hardware simplicity enabled by its single-task purpose, allowed the team to rapidly develop and deploy the robotic system on-site within a year which would have not been possible with generic hardware. The paper describes the current stage of development of the robotic system and uses the presented robotic workflow to outline the benefits of single-task robotics approach in construction.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".