National Strategy for Protecting the Infrastructure: A Visionary Approach
Bibliographic record
Abstract
Does the August 2003 power outage in the Northeastern United States and Canada indicate how vulnerable our critical national infrastructure is to failure? This SRP addresses such concerns about the viability of our critical national infrastructure using dams as a case study. The question therefore becomes how the nation should protect infrastructure whose destruction or dysfunction poses a threat to our citizens not from terrorism but rather from years of maintenance neglect. This SRP considers the vulnerability of the nation's infrastructure from this broader perspective and offers a visionary approach to resource allocation to deal with the challenges facing our infrastructure. This paper will briefly review the Critical Infrastructure Strategy and illustrate how it falls short of achieving a more secure infrastructure because the ways chosen are insufficient to bring about the ends. The focus is on the resource allocation process since it is arguably the most important of the common ways to fully leverage investments in infrastructure. Dams serve as an excellent case study because of their complexities including ownership issues access to funding and current material condition and age. Finally how the proposal can be implemented within the federal budget.
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.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".