Bibliographic record
Abstract
Holography is a photographic technique that records the light scattered from an object,and presenting it in a way that appears three-dimensional (3D). This report gives a brief explanation of recording and displaying of a reflection hologram and aims to \n \n document the three different methodologies from capturing of 3D images to printing them using external holographic printer. First methodology is 3D objects generated using computer-aided design (CAD) programs such as Google Sketch to be printed as 3D Holograms, Second methodology is 3D images captured using 3D Profilometer to be printed as 3D Hologram and the Third methodology is series of images which is recorded using a webcam camera to be printed as 3D hologram.The first and second methodology’s 3D hologram is printed with the technology of Zebra Imaging using application such as SketchUp and ZScapeTM Preview. On the other hand, the third methodology’s 3D hologram is printed with the technology of General Optics Laboratory (GEOLA) using application such as i-LumoLAB. Methods to use these applications will be illustrated for better understanding. Both the process and technology of Zebra Imaging and GEOLA to print a 3D hologram will also be mentioned respectively. To further add on, the technology currently used by the University of Toronto will also be briefly mentioned. A summary for each methodology mentioned above will be shown with an aid of a flowchart, showing the resulting hologram images printed by Zebra Imaging and GEOLA.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".