Interactions between the atmosphere, cryosphere and ecosystemsat northern high latitudes
Bibliographic record
Abstract
Abstract. The Nordic Centre of Excellence CRAICC (CRyosphere-Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, was the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic Region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual Centre with the objectives to identify and quantify the major processes controlling Arctic warming and related feedback mechanisms, to outline strategies to mitigate Arctic warming and to develop Nordic Earth System modelling with a focus on the short-lived climate forcers (SLCF), including natural and anthropogenic aerosols. The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special-issue of the journal Atmospheric Chemistry and Physics. This manuscript presents an overview on the main scientific topics investigated in the Centre and provides the reader a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Facing the vast amount of outcomes we are not claiming to cover all results from CRAICC in this manuscript but concentrate here on the main results which are related to the feedback loops in the climate change-cryosphere interaction scheme affecting the Arctic amplification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".