Element cycling and aquatic function in a changing Arctic
Bibliographic record
Abstract
Abstract Arctic systems are under intense pressure from anthropogenic activities, with climate change in particular inducing rapid change in the interlinked cycling of water and various biogeochemical constituents, and thus also the ecological processes that depend on these cycles. This special issue for Limnology and Oceanography explores our changing Arctic, with contributions across the watershed‐lake‐river‐estuary‐coastal‐open ocean continuum, and foci ranging from physical and chemical processes to food webs. Some specific areas of focus include legacy pollution from mines, greenhouse gas emissions from lakes, riverine fluxes of materials, as well as the balance between primary production and respiration in the water column and benthos in marine systems. While varied in focus, as a collection the papers in this special issue do provide direction into key avenues for future effort. For example, while Arctic systems are historically understudied due to financial and logistical costs, long‐term monitoring efforts are clearly critical for documenting change, despite the challenges. In freshwater systems, predicting biogeochemistry, and thus ecology, based on landscape characteristics and lake morphology is an ongoing practice that seems particularly promising for both upscaling and decisions on focusing future research effort. In marine and coastal systems, complementing specific local studies with large‐scale cross‐disciplinary monitoring programs is clearly required for elucidating long‐term trends. While baseline research is critical for documenting the Arctic as it currently stands, and constitutes the majority of current research efforts, ongoing support for long‐term observatories and expanding remote sensing capabilities is a fundamental requirement for tracking change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".