Development of LiDAR Database Management System using Open Source Software
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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".