Development of LiDAR Database Management System using Open Source Software
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
This study is focused on the development of a LiDAR database management system for the Department of Survey and Mapping Malaysia (JUPEM) to facilitate user authentication, retrieval of LiDAR datasets, storage, scheduling of LiDAR flight sessions and generation of new data products. Additionally, the design goal of the data management system is to support managers-vendors relationship, as well as new data generation out of the results. In this study, we described the structure development of LiDAR database management system and how the system collaborated with data production and data acquisition. The architecture of such application in WebGIS, providing map interactivity in displaying a simple dataset of LiDAR data by using Open Layers integration via GeoJSON format as the spatial data were used to show the implementation of such feature. We also used the Open Source Software (OSS) in the development of the LiDAR Database Management System for JUPEM. This was because many countries, especially in the public sector, were slowly transferring from using proprietary software to OSS.
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.
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 | 0.002 | 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 | 0.001 | 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