Sustainability nexus AID: landslides and land subsidence
Bibliographic record
Abstract
Landslides and land subsidence pose significant threats that are both existing and growing in nature. These complex phenomena should not be considered in isolation but rather as interconnected challenges. To effectively understand and mitigate them, a data-driven nexus approach is necessary. Recognizing the importance of addressing this issue comprehensively, the United Nations University has launched the Sustainability Nexus Analytics, Informatics and Data Programme, a comprehensive initiative that intends to enable the nexus approach to problem solving in coupled human–environment systems. This paper provides a detailed background on the Programme’s “Landslides and Land Subsidence Module”, underscoring the crucial need for a nexus approach. Additionally, it highlights some of the tools and strategies that can be employed to tackle the challenges at hand. The success of this initiative hinges on active participation from various stakeholders. By embracing a holistic approach and fostering collaboration, we can strive towards better preparedness and long-term resilience against landslides and land subsidence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".