Quality of Regulatory Pond Development Plan Documents for Barabai Flood Control Against Mandatory LoadsLand Acquisition Planning Document
Bibliographic record
Abstract
The implementation of land acquisition for the construction of the Barabai flood management pond has been completed successfully and is regarded as a success. One of the factors influencing the success of its execution is land acquisition planning, as stated in the Land Acquisition Planning Document (DPPT). The goal of this research was to assess the quality of the mandatory cargo in the Regulatory Pond Development Plan Document for Flood Control of the Barabai River for the Fiscal Year 2021. The quality of the mandatory cargo for the DPPT is determined using a qualitative technique with descriptive analysis in accordance with Ministerial Regulation Spatial Planning (ATR) /Head of the National Land Agency (BPN) No. 19 of 2021. The document was recognized and examined based on the regulation's mandatory content. Document studies were conducted to acquire data by studying the contents of the DPPT. The study revealed that there are 38 descriptions that must be met in order to create the DPPT. A total of 29 descriptions in the planning document have been thoroughly examined in their analysis, while nine descriptions require further discussion in the document. The presence of more favorable than bad descriptors in the DPPT implies that the stages of land acquisition planning and implementation are in sync. The presence of more favorable than bad descriptors in the DPPT implies that the stages of land acquisition planning and implementation are in sync. Mean¬whi¬le, the nine descriptors must be examined in greater depth in the document. The presence of more favorable than bad descriptors in the DPPT implies that the stages of land acquisition planning and implementation are in sync.
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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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".