Mapping International Trade and Supply Chains for Humanitarian and Business Resilience to Atmospheric and Pandemic Disasters Method and Preliminary Findings for Puerto Rico
Bibliographic record
Abstract
The study presents preliminary findings of a Pilot Project that identified alternatives to disruptions in international trade and supply chains in the face of atmospheric (hurricane Maria in 2017) and pandemic (COVID-19) disasters. It focused on four critical imports: water, humanitarian relief goods, solar photovoltaic products, and COVID-19 test reagents. The project proposed an alternative pathway and method to address disruptions: build an integrated database from Bill of Lading and import-export-related data organized by Harmonized Schedule Code. Data analyses were carried out based on the Harmonized Tariff Schedule code system, and an integrated database was generated for imports of the four products that Puerto Rico buys in the international market. Import Key data were analyzed based on the Bill of Lading, sector, companies that sell the product, location of countries where the products are sold, ports that can reach Puerto Rico, and contact information of the companies and suppliers. This information allows humanitarian organizations, SMEs and the government to identify alternative supply networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".