Photogrammetric Modeling of Monuments Eflatunpinar (Lilac-Coloured Spring)
Bibliographic record
Abstract
SUMMARY Lilac-Coloured Spring monument the shores of Lake Beysehir, within the limits of the town of Konya, dated to the late Hittite about B.C1300 years, and in particular a monument containing the remains of an archaeological field. In this study, have a point cloud and surface models of monuments, which is located in Beysehir district of Konya in Turkey, has been examined by digital close range photogrammetry. PhotoModeler (Eos Systems, Inc. Canada) Scanner includes new skills in a module called Dense Surface Modeling (DSM). The DSM generating from photo pairs great number of 3D point clouds, this automatically creating a point cloud same as laser scanning technique. The DSM technology needs minimum two images (stereo pair) by digital cameras for create the three-dimensional (3D) model. Camera setup is an important factor for capturing high-accuracy data because several pairs of synchronized digital cameras are needed to capture the images for craniofacial area of subject. As a result, a research was figure out creating the monument with high-accuracy point cloud. This paper provides a detailed discussion of how to using photogrammetric scanners for the monuments, the procedures to process stereo-pair images and the evaluation of the 3D model visualization reconstruction using photo-based scanned data. OZET
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".