Visualization and Feature Extraction of the Surface Morphology of the Abdomen of Red Swamp Crayfish
Bibliographic record
Abstract
This paper demonstrates a method for visual reconstruction and feature analysis of the surface morphology of red swamp crayfish in CATIA (Computer Aided Three Dimensional Interactive Application) and Microsoft Excel. Red swamp crayfish, Procambarus clarkii, with efficient burrowing activities and coupling propel pattern of abdomen with tail, was selected to study the feasible methods in the visual reconstruction and feature analysis of the surface morphology of living things. The digital measurements of surface of the red swamp crayfish were carried out using a three-dimensional laser scanner. Point clouds, the scanning digital data of the surface of the red swamp crayfish, were processed by deleting unwanted data, reconstructing surface in CATIA. There was a perfectly shape character similarity between the digital picture of the abdomen with corresponding point clouds shown in CATIA, and transverse curves which shown the surface morphology of abdomen in the cross section along the red swamp crayfish were obtained and saved as files of ASCII format in CATIA. Feature analysis of the abdomen of red swamp crayfish were carried out after files of single transverse curve were imported into Microsoft Excel, results shown that, the first row in file of single transverse curve was the number of rows after it, and those other rows stored coordinate values of measured points of the abdomen in the preset three-dimensional coordinate system, shapes of the abdomen in different cross sections were similar, and quadratic polynomial regression equation was able to effectively express surface morphology of the abdomen of red swamp crayfish. Methods and results presented in this paper prove to be potentially useful for analyzing the feature of biological prototype, optimizing the mathematical model and affording deformable physical model to bionic engineering, those works would have great implications to the research of biological coupling theory and technological creation in bionic engineering. Key words: Visual reconstruction; Feature analysis; Surface morphology; Red swamp crayfish
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".