GEOSPATIAL INFORMATION NETWORK GEOPORTAL DEVELOPMENT IN SUPPORTING DATA AVAILABILITY AND IMPROVING HUMAN RESOURCE PERFORMANCE IN GRESIK REGENCY
Bibliographic record
Abstract
In the management of the regional geospatial information network, evaluation needs to be carried out as a continuous improvement effort. The assessment focuses on both internal and external aspects in the process of geospatial information management. The purpose of this study is to determine the readiness of the development of the geoportal, the availability of data and human resources in Gresik District, as well as to identify the strengths and weaknesses. The method used in this research is descriptive analytic method, which identifies, evaluates, and assesses each instrument through data scanning and interviews. The results of this study show that the Gresik District Geospatial and Geodetic Network Node has achieved an "Operational" status, compared to before when it did not exist. Gresik District is now joined by 38 other districts/cities with an "Operational" status. The evaluation value falls into class B, which means the maximum development time is 12 months. In addition to the availability of the geoportal, Gresik District also has a data forum, but there is no Spatial Data Forum, so in the future the need for this forum becomes very important to support the improvement of the network node in Gresik District.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".