Development of the National Environmental Sustainability and Technology Tool (NESTT) : a sustainability measurement and management platform for Canadian egg farmers
Bibliographic record
Abstract
The global agri-food system is necessarily central to contemporary sustainability discourse due to its significant contribution to major environmental issues and livestock-based food production is particularly impactful and considered among the top three contributors to most serious global environmental issues. As a result, there is growing consumer and regulatory demand for better sustainability measurement and management capabilities in the agri-food sector. Farm-level sustainability decision support tools characterized by their ease of access, simplified sustainability assessment models, and emphasis on effective communication and aesthetic appeal are particularly salient in this context. The dissertation research reported herein culminated in the development of a tool with such characteristics – the National Environmental Sustainability and Technology Tool (NESTT) – which was developed for the Canadian egg industry. NESTT can help Canadian egg farmers measure their farm’s environmental impacts, evaluate the mitigation potential of sustainability technologies and strategies, benchmark their performance against industry standards, and track their performance over time. The sustainability assessment framework in NESTT was based on Life Cycle Assessment (LCA). A new allocation method based on metabolic energy partitioning in hens was developed to address multifunctionality (a major methodological consideration in LCAs) in NESTT in way that is compliant with the natural science basis and requirements of the ISO 14044 standard for LCA. For measuring impacts, a modular approach based on multi-level aggregation of life cycle impacts across six modules – pullets, feed, energy inputs, water, manure management, and transportation – was implemented. To ensure that NESTT meets the requirements of farmers, a participatory design approach involving surveys and interviews of egg farmers was employed. Improvements related to user-centeredness, aesthetic appeal, and accessibility were achieved and long-term strategic options such as integrating economic assessments and adding more customized decision support features were identified in consultation with farmers. In support of these long-term goals, a preliminary economic assessment framework was also developed and integration of Multi Criteria Decision Analysis (MCDA) methods with LCA for customized decision support was explored. Overall, NESTT provides a unique, first of its kind tool to provide farmers with multi-criteria, LCA-based assessment capabilities in both Canada and the egg sector globally.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".