Snapshots of the World’s Cold Regions Changing Biogeochemistry
Bibliographic record
Abstract
The Earth's cold and cold-temperate regions are undergoing deep and accelerating changes due to climate change. Warming in moderate-to high-latitude terrestrial and aquatic ecosystems is accompanied by permafrost thaw, shorter winters, reduced ice cover, earlier snowmelt, more intense soil freeze-thaw cycles, drier summers, and longer fire seasons. These environmental changes in turn impact surface water and groundwater flows, water quality, greenhouse gas emissions, soil stability, primary production, and (micro)biological communities. Warming also facilitates agricultural expansion, urban growth, and natural resource development, adding growing human pressures to cold regions' water resources, soil health, and biodiversity. In this presentation, I will provide some snapshots that illustrate several of the challenges, knowledge gaps, and opportunities in cold region biogeochemical research, with an emphasis on processes and responses in both natural and anthropic environments. Many of the biogeochemical changes are closely interrelated with the changing hydrological and thermal regimes affecting cold regions' landscapes and water bodies. Compared with their temperate counterparts, these landscapes and water bodies experience shorter growing seasons, more persistent ice and snow covers, extensive permafrost, pronounced cycles of freezing and thawing, and intensifying physical and chemical weathering. Here, I will focus on some of the more unique features and response dynamics of cold regions' biogeochemistry. In addition to new unpublished work, my presentation will cover material from [1][2][3][4].
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".