Towards sustainable agricultural landscapes: Lessons from an interdisciplinary research-based framework applied to the Saint Lawrence floodplain
Bibliographic record
Abstract
Floodplains are unique environments that provide a dynamic link between terrestrial and aquatic systems. Intensification of human activity – particularly agriculture and urbanisation – has resulted in the degradation of floodplains worldwide. Restoration and sustainable management of floodplains requires holistic assessment and compromise between stakeholders to successfully balance environmental, economic, and social benefits. Yet, understanding these complex systems sufficiently to provide evidence-based recommendations is a challenge. We present the lessons learned from establishing an interdisciplinary research-based framework on the agricultural floodplain of Lake Saint Pierre, Québec, Canada, whose mandate was to a) understand and define key environmental, agricultural, and socioeconomic attributes of the landscape, b) quantify the trade-offs and synergies between these attributes across different agricultural practices, regions, and land uses, and c) explore novel agri-environmental management practices to assess their role in sustainable floodplain management. Within this manuscript, we explore the benefits that such an approach offers in evaluating sustainable floodplain land use. We found that an interdisciplinary research-based approach demonstrated important benefits such as knowledge transfer, more efficient use of resources (e.g., personnel, funding), and a flexible yet robust research framework. A framework of individual research projects connected to broader interdisciplinary themes allowed a more holistic synthesis of the floodplain systems and assessment of agri-environmental practices. By implicitly considering spatial and social scales, we conceptualised not just how redistribution of the land use types can meet sustainable management objectives, but also explored how compromises within existing uses can optimise socio-economic, agricultural and environmental dimensions and move towards a sustainable multifunctional landscape.
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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 | 0.023 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.068 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".