A Farm‐To‐Fork Framework to Assess the Scope and Limitations of Agricultural Data Structures
Bibliographic record
Abstract
ABSTRACT Farming methods efficiency, agrifood systems sustainability, food traceability, and supply chain transparency depend on robust data management systems. However, current agricultural data structures (schemas, models, frameworks, file systems, etc.) and infrastructures remain disjointed across pre‐ and post‐harvest processes, often focussing on certain supply chain stages. This paper contributes an assessment of the scope and limitations of current agricultural data structures through a new proposed framework named AgrIMAF (Agricultural Information Model Assessment Framework). AgrIMAF is a three‐layered framework composed of (a) supply chain stages, (b) stakeholders, and (c) data flows produced and required by stakeholders across the chain, each serving as a criterion to assess agricultural data structures identified through a systematic literature review. We assessed 30 data structures with AgrIMAF, revealing a predominant emphasis on preharvest stages, while postharvest stages are markedly underrepresented. Stakeholders such as customers, insurers, dietitians, and waste managers were predominantly neglected in the investigated data structures. The analysis indicates extensive coverage of crop, weather, and soil data, however post‐harvest categories such as traceability, marketing, consumption, and waste are frequently absent. Sustainability initiatives and biodiversity metrics are infrequently acknowledged. AgrIMAF provides a diagnostic instrument to evaluate information systems and enhance sustainable, transparent supply chains.
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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 | 0.048 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.026 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".