Selected topics in arctic atmosphere and climate
Bibliographic record
Abstract
Abstract This paper summarizes the main elements of four IPY projects that examine the Arctic Atmosphere. All four projects focus on present conditions with a view to anticipating possible climate change. All four investigate the Arctic atmosphere, ocean, ice, and land interfacial surfaces. One project uses computer models to simulate the dynamics of the Arctic atmosphere, storms, and their interactions with the ocean and ice interface. Another project uses statistical methods to infer transports of pollutants as simulated in large-scale global atmospheric and oceanic models verifying results with available observations. A third project focuses on measurements of pollutants at the ice-ocean–atmosphere interface, with reference to model estimates. The fourth project is concerned with multiple, high accuracy measurements at Eureka in the Canadian Archipelago. While these projects are distinctly different, led by different teams and interdisciplinary collaborators, with different technical approaches and methodologies, and differing objectives, they all strive to understand the processes of the Arctic atmosphere and climate, and to lay the basis for projections of future changes. Key findings include: • Decreased sea ice leads to more intense storms, higher winds, reduced surface albedo, increased surface air temperature, and enhanced vertical mixing in the upper ocean. • Arctic warming may affect toxic chemicals by remobilizing persistent organic pollutants and augmenting mercury deposition/retention in the environment. • Changes in sea ice can dramatically change processes in and at the ice surface related to ozone, mercury and bromine oxide and related chemical/physical properties. • Structure and properties of the Arctic atmospheric—troposphere to stratosphere—and tracking of transport of pollution and smoke plumes from mid-latitudes to the poles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".