A Novel Approach for Porcupine Crab Identification and Processing Based on Point Cloud Segmentation
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
Despite the increasing application of automated processing equipment in commercial seafood industry, such as large-scale Latin fish and snow crab production lines, manual laboring method dominates in current seafood processing, resulting in low production rate and increased cost. Among various types of seafood crabs, porcupine crabs have shown potential for quality marketable crab meat products. However, their long, sharp spines pose significant challenges for manual laboring and thereby call for robust automated system for processing. In this paper, using 3D point cloud data of the porcupine crab as the input, a novel robot-based approach is proposed to generate the robot trajectory for spine removal. This approach has been validated via a simulation example using ROS (Robot Operating Systems). The proposed method can be introduced into many other manufacturing processing, including polishing, painting, grinding and deburring for work pieces with complex surfaces.
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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 | 0.000 | 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