6.4.1 On the Use of Knowledge Modeling Tools and Techniques to Characterize the NOAA Observing System Architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The National Oceanic and Atmospheric Administration (NOAA) in the United States has a very broad charter that includes making “observations” of environmental phenomena worldwide. These observing systems are used to provide accurate and timely information to various stakeholders who depend on this information to make critical decisions about farming, construction, fishing, military operations, traffic safety, and so on. This paper describes a project to develop the NOAA Observing System Architecture to assist NOAA in formulating its strategic plans and invest its funding more effectively. We used “knowledge modeling” as the basic approach to capture information about the systems and other entities associated with the architecture. We are using the Metis enterprise architecture tool along with DOORS for requirements, NOAA Forge for team collaboration, and ArcIMS for geospatial data visualization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it