A Methodological Approach to Designing Circular Economy Indicators for Agriculture: An Application to the Egg Sector
Bibliographic record
Abstract
Analysing production systems from a circular economy (CE) perspective helps to pinpoint interventions to mitigate the environmental footprint by improving resource use efficiency, waste recovery, and prolonged product usage, recycling and reuse. Few studies exist on the measurement of CE at the micro-level. Additionally, available metrics/indicators address only certain aspects of the CE’s socio-economic metabolism, ignoring important components of the CE concept. Other frameworks propose a single indicator that aggregates and summarizes several facets of CE. This study develops a holistic approach for designing indicators with a structured methodology and an analytical framework to assess CE at the micro (unit of production) level in agriculture. The proposed approach is based on the ECOGRAI method for indicator development, and on validation of the methods with experts and final users via an application to egg production in Canada. Twenty-five performance indicators (PI) were generated for 11 decision variables that were selected as important for the sector. This resulted in a practical tool that proposes fourteen actions to improve the economic circularity (EC) of egg farms. Our methodological approach could be replicated to assess CE performance in other agricultural sectors.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".