The 3D marine alienation title for marine cadastre in Kedah and Perlis / Nur Liyana Mat Rosdi
Bibliographic record
Abstract
The introduction of the marine cadastre in Malaysia is still in its infancy even up to date compared to other countries such as Australia, United States and Canada where the country has already been approaching with the introduction of marine cadastre in advance of us. However, Malaysia is still missing the references and sources of the marine cadastre applications. But, Malaysia is still not yet implemented the marine title documentation in Malaysia. The aim of this study to identify the best practice for 3D marine alienation title for marine by study case in Kedah and Perlis. The objective that come out parallel with the problem is to understanding a 3D marine alienation title for marine cadastre documentation, to create a 3D of marine in marine documentation for marine cadastre with buffer 3 nautical miles from low tide water and to propose a document of marine alienation for marine cadastre. The method that used in this research study is recreate sample of marine title documentation include with Qualified Title plan, generate 3 nautical miles shorelines in Map Info software and did data verification and testing by distributed the questionnaires and discussion among the agencies involved with marine cadastre. Finally, the end of result is produces a marine title documentation which distinguishes between a marine title and land title by attaching Qualified Title plan in two dimensions namely 3D and 2D by obtaining the approval of the marine cadastre specialist regarding the production of the grant sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".