HARMONIZING GROWTH: EXPLORING THE WATER-ENERGY-FOOD NEXUS THROUGH AGRICULTURAL INNOVATION AND CIRCULAR ECONOMY IN A BOREAL ECOSYSTEM
Bibliographic record
Abstract
The global commitment to the right to food, enshrined in international legal frameworks such as the UN’s Rights to Adequate Food Guidelines, reflects its status as a fundamental human right, an assertion supported by numerous national constitutions and policies (Clapp et al., 2022). Addressing the profound challenge of nourishing the ever-expanding global population underscores the central role of agriculture as the primary vehicle for food provision. However, the production, processing, and distribution of food involve the utilization—often overexploitation—of critical resources, including water, energy, and land, contributing significantly to issues such as pollution and climate change (Mor et al., 2021). This study delves into the critical interplay between the right to food, agricultural practices, and their environmental repercussions. Highlighting the intricate balance required for sustainable food production, the research aims to contribute to the discourse on navigating the challenges inherent in meeting the nutritional needs of a growing global population. Recognizing the multifaceted importance of agriculture, the paper emphasizes its foundational role in ensuring the existence, survival, and economic well-being not only of humans but also of diverse ecosystems. Beyond its primary function of providing sustenance, agriculture assumes pivotal roles in sustaining national economies, offering employment opportunities, and fostering livelihoods, particularly in rural areas (World Bank, 2006, 2012). By exploring these dimensions, the study seeks to provide nuanced insights that can inform policies and practices, fostering a harmonious integration of agricultural development with environmental sustainability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".