SISTEM INFORMASI REKAPITULASI DATA REALISASI INVESTASI DI DINAS PENANAMAN MODAL, ESDM DAN TRANSMIGRASI PROVINSI GORONTALO
Bibliographic record
Abstract
Abstract The recruitment system is essential to improve service to a job or activity. The activity of collecting data on the realization of investment at the Gorontalo Province Investment, ESDM, and Transmigration Office uses Microsoft Excel. This recapitulation activity has several obstacles that are the background of this research. The constraints experienced are the slow process of recapping, piling up of files so that there is a risk of damage or loss of data, and unable to display investment charts every quarter. The method used in this research is the prototype method. The final results obtained in this study are in the form of an information system for recapitulation of investment realization data to make it easier for the government to manage recapitulation activities, monitor the progress of investment every quarter, and be able to determine the budget plan to be set for the following year and data storage to be more effective and safer. Keywords : Recapitulation, Recapitulation Information System, Realization of Investment, Investment, Prototype.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".