GEOGRAPHYCAL INFORMATION SYSTEM (GIS) BASED EMPLOYEE MAPPING PT ASTRA DAIHATSU MOTOR
Bibliographic record
Abstract
PT Astra Daihatsu Motor consisting of Stamping Plant, Engine Plant, Casting Plant, Assembly Plant, Head Office and Part Center that contain many employee about nine thousand spread at JABODETABEK'S region. Since often its happening disaster as accident of flood at territorial JABODETABEK makes PT.Astra Daihatsu is Motor as corporate as professional, issuing policy to give compensation to employee one strikes that accident. Human Resources Development (HRD) that have task in brings off and service employee perceive handicap to give compensation to employee in point since haven't marked sense data about employee domicile.To it at makes database that valid to know employee mapping at a given location. Method that is utilized in final task writing this by use of modelling tool as Flow is Folder (Documents Flowing charts), DFD (Flow's Data Diagram) and data collecting tech with pervading form domicile, observation trick, and interview. Meanwhile application development tool database utilizes Mysql and with programming languages PHP for webite's application and Flash for map. Result to this scheme is one System which given by name Geographycal Information System (GIS) Based Employee Mapping PT.Astra Daihatsu is Motor .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.026 | 0.011 |
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".