Developing a Software Tool to Estimate Food Transportation Carbon Emissions
Bibliographic record
Abstract
Food transportation is an increasingly important consideration to total food sustainability in a rapidly globalizing world. To maintain the efficiency of regionalized production, food travels great distances to the consumer’s plate. While this long-distance sourcing is often more sustainable from a production standpoint, the routes from origin to consumer are frequently unoptimized. To reduce emissions due to transportation, many have tried to limit the miles travelled by food items. However, the mode of travel is an equally important factor. Different modes produce vastly different emissions over equivalent distances. To effectively model these routes, a set of transportation emissions estimation tools has been created. This program uses an Excel interface to allow users to input key factors (like cargo mass, origin, and destination) and experiment with different modes and routes of travel to find the optimal transportation system for their application. This program may be used to analyze or improve the total life cycle analysis of a variety of products. In a case of the comparison of transportation modes, a salmon transportation route from the Faroe Islands (America’s 2nd largest source of imported fresh salmon) to Richmond, VA, USA, resulted in a roughly 98% reduction of emissions when shipped via sea rather than flown. In a case of transportation optimization, the reciprocal trade of beef between Costa Rica and the United States was found to result in at least 158,000 kg of CO2eq annually. These cases (and others) show the great need for better route optimization in food transportation systems.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".