RANCANG BANGUN KNOWLEDGE MANAGEMENT SYSTEM BERBASIS WEB (PENGELOLAAN SUMBER DATA SEBAGAI PENDUKUNG PENGETAHUAN)
Bibliographic record
Abstract
This Project discuss assembling about application portal knowledge management in higher university, like we know about in higher university is location to interaction for people studied. Various to follow open knowledge. The higher university have had competition level and high demand form stakeholder, that is increase quality although transparency in management organization. With competition is very expert, the higher university must have skill to absorbknowledge that had civitas academic, in order that knowledge who there is in area university can kept with explicit, shaped to make dissemination knowledge. To reach necessity about that project so did program architecture knowledge management for system to used in tools as support management knowledge management in higher university. Knowledge management had facilities to made collaboration, repository and dissemination knowledge who have in every civitas academic, in example is case Politeknik Pos Indonesia. The planning started with observe necessity system, specification and software and grow up prototype where able bridge between running system to resource data with knowledge management portal is development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".