Bibliographic record
Abstract
In this paper, a strategy to accomplish small stone carving operations using a four-degree-of-freedom (DOF) robotic arm is presented. This research work tries to design a novel robotic arm that is small enough that can be mounted on the table and create artwork by carving and shaping stones. In the past two decades, there were many different designs for the robotic arm from simple 3DoF serial manipulator to advanced hybrid one, but the main thing in those designs was that they have been designed in order to use on an industrial scale. In this design what we have considered as a priority is to create a manipulator that can be mounted on a small table and also could be used by nonprofessionals. The designed robot, CarveARM, has a unique End Effector (EE) which is rotating around itself so fast to carve a stone precisely. At first, by gathering the link transformation matrices between each joint and also using the Denavit-Hartenberg (DH) notations the CarveARM manipulator coordinate transformation is completely described. We have also used some robotics methods to achieve some particular tasks such as solving the forward kinematics description, inverse kinematics problem, workspace evaluation, and dexterity analysis. Besides the position control of EE, we also control the final angle of EE to handle the stone carving procedure more accurately.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".