Socioecological dynamics of diverse global permafrost-agroecosystems under environmental change
Bibliographic record
Abstract
Permafrost-agroecosystems include all cultivation and pastoral activities in areas underlain by permafrost. These systems support local livelihoods and food production and are rarely considered in global agricultural studies but may become more relevant as climate change is increasing opportunities for food production in high latitude and mountainous areas. The exact locations and amount of agricultural production in areas containing permafrost are currently unknown, therefore we provide an overview of countries where both permafrost and agricultural activities are present. We highlight the socioecological diversity and complexities of permafrost-agroecosystems through seven case studies: (1) crop cultivation in Alaska, USA; (2) Indigenous food systems and crop cultivation in the Northwest Territories, Canada; (3) horse and cattle husbandry and Indigenous hay production in the Sakha Republic, Russia; (4) mobile pastoralism and husbandry in Mongolia; (5) yak pastoralism in the Central Himalaya, Nepal; (6) berry picking and reindeer herding in northern Fennoscandia; and (7) reindeer herding in northwest Russia. We discuss regional knowledge gaps associated with permafrost and make recommendations to policy makers and land users for adapting to changing permafrost environments. A better understanding of permafrost-agroecosystems is needed to help sustainably manage and develop these systems considering rapidly changing climate, environments, economies, and industries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".