Malaysian Land Administration Domain Model Country Profile
Bibliographic record
Abstract
Land administration is a process of recording and disseminating information about the association between people and land. To administer land matters in Malaysia, the Department of Surveying and Mapping Malaysia uses eKadaster and Land Office has eTanah which are different in e-Systems. Currently, Malaysia does not have a standard model for land administration and standardisation is one of the important aspects in a land administration process. This paper proposed a country profile model using international standards based on Land Administration Domain Model. This paper also attempted to generate strata object model via Land Administration Domain Model which would be useful for Malaysia and countries with similar land administration systems. In this proposed model, spatial data modelling using secondary data from the aforementioned two land administration units in Malaysia and Unified Modelling Language application were used to develop the conceptual and the technical models. The developed model was evaluated and verified by the Department of Surveying and Mapping Malaysia and Land Office. These units agreed and were satisfied because the model fits their requirements by being more comprehensive as it included three-dimensional lots and two-dimensional topology. In addition, the proposed model facilitated the management of spatial and non-spatial objects such as customary areas, reserved lands, lots, strata objects, utilities and the related attributes to be better managed by the two units. The development of Malaysian Land Administration Domain Model country profile is unique because it can support a very wide range of spatial units. Furthermore, the model was developed to help establish a national Spatial Data Infrastructure. To conclude, the developed Malaysian Land Administration Domain Model is a standardised model that could be used for local and international exchange of information concerning land administration matters.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".