2.1.1 Using a Knowledge Management Tool to Improve U.S. EPA's Enterprise Architecture
Bibliographic record
Abstract
Abstract Enterprise architecture (EA) is a process for ensuring that an organization's information resources are aligned with its goals and business. EA can be used for several purposes, such as improving business processes that are performed across an organization and integrating information management resources. For EA to achieve its potential, it is important to use an EA tool for housing and analyzing the organization's information. The U.S. Environmental Protection Agency (EPA) is using such a knowledge management tool, called the Architecture Repository and Tool (ART), that is based on Metis software from Computas. ART can be used to identify which organizations perform the same business processes and to highlight opportunities for storing commonly used data. This paper illustrates how ART is a knowledge management tool that can reveal an organization's gaps, weaknesses, and opportunities for improvement.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".