Piecing Together Prehistoric Life: Scanning and Articulating Gorgosaurus
Bibliographic record
Abstract
The Skull bones of a Gorgosaurus Libratus was laser scanned in order to articulate the model into software and 3D print. The model had to be articulated due to some missing bone, making it unrealistic to put together. Using the scanned pieces we articulated the model making a skull of The Gorgosaurus Libratus. This detailed computer skull can be sent anywhere in the world, for anyone to study. These scans could also be used to find out how the Gorgosaurus Libratus bit down or determine the way these animals moved. Prior to laser scanning, a method known as Photogrammetry was used. This method involves taking photos of the model and processing the images on a computer, which slow down the process. Another way used to replicate bones was by making silicone molds. This could damage the bone which makes it a method used less often. Laser scanning is the fastest and safest method in order to scan a bone. After the bones were articulated on the computer they were sent to a 3D printer. Unfortunately, the printer beds could not hold the massive skull. Due to this, the bones were printed half size. In order to 3D print, the holes of the model had to be filled using another program. The holes were caused by the light of the laser scanner not being able to go into all the holes creating shadows that the laser scanner could not pick up. However, after the holes were filled some of the objects were still too big to fit on the printer bed. Therefore, some of the objects were cut in half to fit. The 3D printed models were then printed and assembled.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".