Bibliographic record
Abstract
Abstract Modern food supply chains—infused with scientific and engineering innovations—have made food increasingly more affordable and accessible. Yet there is growing concern about the long‐term sustainability of our food system. Over time, the inputs (e.g., water, fertile soil, fossil fuels, and chemicals) and working resources (e.g., land and labor) required for industrial food production and its associated supply chain structure have become more scarce and hence more expensive. At the same time, the by‐products of these farming and supply chain activities (e.g., farm runoff and greenhouse gas emissions) have often created negative externalities on the environment and human health. To improve the sustainability of food production, research from the life sciences recommends adoption of transformative farming methods that incorporate ecological principles in a sustainable approach to farming. Operationally, this approach leverages economies of scope . In order to maintain strategic alignment, changing food production methods should be complemented with appropriate changes in the rest of the supply chain, including consumption habits. We propose a research agenda informed by findings from the life sciences, which integrates approaches from supply chain management as well as food and agricultural economics, to align all food supply chain partners with sustainable food production.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".