Bridging the gap in sustainability measurement and reporting for agroecosystems: Overview and development of an adaptive sustainability assessment and monitoring framework
Bibliographic record
Abstract
Measuring sustainability in agroecosystems is inherently complex due to the diverse and dynamic nature of agricultural sustainability indicators. Traditional assessment tools often rely on universal objectives and baselines, which can obscure immediate problems and hinder effective sustainability efforts. In this paper, we propose an Adaptive Sustainability Assessment and Monitoring Framework (ASAMF) intended to address some of these limitations by starting with a clearly defined sustainability objective. The framework categorizes sustainability indicators and selects those relevant to each category, establishing a site-specific baseline against which actual farm measurements are compared. This approach offers a nuanced understanding of a farm’s sustainability relative to its potential capacity, highlighting areas for targeted improvement. The proposed framework is dynamic and adaptable, allowing for the evaluation of sustainability based on region-specific objectives and indicators rather than absolute metrics. This flexibility facilitates meaningful comparisons across different geographic locations and farming practices, enabling a pragmatic assessment of agroecosystem performance. By aligning agricultural practices with sustainability goals, the framework supports the transition towards more sustainable agroecosystems. This paper explores the conceptual foundations of sustainability and engages with existing measurement approaches, presenting the structure of the Adaptive Sustainability Assessment and Monitoring Framework. The framework’s practical applications and broader implications are discussed, demonstrating its potential to guide policy decisions and advance global sustainability initiatives. Through this comprehensive examination, we aim to provide a robust and practical framework that can enhance the assessment and monitoring of sustainability of agroecosystems.
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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 | 0.128 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".