Review of Pastoralists’ Resilience and Adaptation to Climate Change: Can Technology Help Pastoralists Mitigate The Risks?
Bibliographic record
Abstract
In the changing environments globally, it is essential to look deeply into the effects of climate change on rangelands, pastoralists and livestock grazing, and into how the pastoralists cope with the climatic changes and challenges.There is a scientific rationale behind the pastoralists' adaptation strategies for coping with the climate variability.The present review-based paper explores how the rangelands and nomadic pastoralism are critical for grassland biomes and ecosystems.It is scientifically es tablished that the pastoralist lifestyles are not only most sustainable in present times but also most resilient, given the challenges of climatic variability.As the changing climates globally pose threats to grassland ecosystems and associated natural resources, pastoralists and their livestock are affected greatly by erratic weathers and changing availability of palatable biomass.Available literature proves that the pastoralist people hold much of the knowledge about how to adapt in hostile and varying climates.For example, the pastoralists adopt strategies such as rotational use of pasturelands, division of livestock, diversification of livestock, predicting rainfall and seasonal changes, and so on.In addition to understanding the resilience and adaptation strategies of pastoralists, present paper addresses how the technology might help nomadic pastoralists build their resilience on the face of climate change.This paper finally discusses the need to test various technologies in biological, ecological and anthropological contexts of the rangelands and pastoralism so that dying lifestyles and cultures of the marginalized nomadic people can survive in hostile climate change regime.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".