Local supply chains: The disaster management perspective
Bibliographic record
Abstract
Situational awareness of the operational status of specific, critical supply and demand nodes following a major disaster may inform response and recovery activities based on the ability of an infrastructure asset or system to support core facility operations.Near-real-time analysis of infrastructure dependency information is a computationally intensive process that has generally been observed informally by public safety officials.While system-level information may be desired, it has been beyond the capabilities of most local public safety and emergency management agencies.To address this problem, a Grassroots Infrastructure Dependency Model (GRID-M) was developed to enable near-real-time analysis of physical infrastructure dependencies of specific supply and demand nodes within four lifeline sectors: electricity, natural gas, water, and wastewater.The operational status of each node can be characterized as operational, partially operational, or not operational.These statuses are obtained by matching real-time outage or disruption data from utility providers with predetermined specific coping strategies based on a preincident limited infrastructure survey for specific assets within a network.This information can also be paired with a limited damage assessment to provide awareness of the accessibility to, and physical state of, each node within supply chains of interest.GRID-M displays all outputs within a Geographic Information Systems environment with additional prepopulated layers such as real-time traffic and demographic information of the affected communities.As such, GRID-M may be used following a major disaster to support the identification of priority response and recovery objectives based on the disruptions of critical local supply chains and their relationship with affected communities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".