Habitability of low-lying socio-ecological systems under a changing climate
Bibliographic record
Abstract
Abstract Climate change will push the planet worryingly close to its boundaries, across all latitudes and levels of development. One question therefore is the extent to which climate change does (and will) severely affect societies’ livelihoods, health, well-being, and cultures. This paper discusses the “severe climate risks” concept developed under Working Group II’s contribution to the Fifth and Sixth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC, AR5, and AR6). Focusing on low-lying coastal socio-ecological systems (LCS) and acknowledging that attempts to define “severe” climate risk have been problematic at the level of global syntheses, we argue for a more place- and people-based framing relating to “habitability under a changing climate.” We summarize habitability in terms of five habitability pillars: land, freshwater, food, settlement and infrastructure, and economic and subsistence activities; we acknowledge social and cultural factors (including perceptions, values, governance arrangements, human agency, power structures) as critical underlying factors rather than as separate pillars. We further develop the habitability framing and examine climate risk to future human health and habitability for three climate “hotspot” archetypes (arctic coasts, atoll islands, densely populated urban areas). Building on the IPCC AR6 framing of severe climate risks, we discuss three key parameters describing severe climate risks in LCS: the point of irreversibility of changes, physical and socio-ecological thresholds , and cascading effects across various habitability dimensions. We also highlight the variability of severe risk conditions both between coastal archetypes and within each of them. Further work should consist of refining the case study framing to find the right balance between capturing context-specificities through real-world local case studies and commonalities derived from more generic archetypes. In addition, there is a need to identify appropriate methods to assess irreversibility , thresholds , and cascading effects , and thus severe climate risks to habitability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".