Embracing complexities in agricultural water management through nexus planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A major challenge for agricultural water management (AWM) in the 21st century is to feed a growing population in the face of increasing intersectoral resource competition, evolving diets, degradation, pandemics, geopolitical conflicts and climate change. This has to be achieved within the planetary boundaries and without compromising the livelihood and environmental (ecosystem) objectives linked to water, including provisioning, supporting and regulating services. This paper uses a systems and nexus lens to unravel the centrality and complexities in AWM, with particular emphasis on the interconnected dimensions and objectives of AWM, as well as its practices and technologies. AWM exists beyond water and food with linkages to human and environmental well-being. AWM needs to catalyse transformation and integrate approaches across systems, users and scales to meet its objectives in a changing climate. It must provide perspectives beyond productivity, managing water risks and safeguarding food security - as important as these are - and integrate our understanding of the interconnected climate, land, water, food and ecosystems to address planetary health outcomes. By doing so, AWM could catalyse contextualised, equitable, innovative solutions that acknowledge local socio-economic and institutional structures and limitations while catalysing sustainable development and climate resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it