Bibliographic record
Abstract
Recent increases in extreme events, especially those near and beyond previous records, are a new index for Arctic and global climate change. They vary by type, location, and season. These record-shattering events often have no known historical analogues and suggest that other climate surprises are in store. Twenty-six unprecedented events from 2022, 2023, and early 2024 include record summer temperatures/heatwaves, storms, major Canadian wildfires, early continental snow melt, Greenland melt, sea temperatures of 5–7 °C above normal, drought in Iceland, and low northern Alaskan salmon runs. Collectively, such diverse extremes form a consilience, the principle that evidence from independent, unrelated sources converge as a strong indicator of ongoing Arctic change. These new behaviors represent emergent phenomenon. Emergence occurs when multiple processes interact to produce new properties, such as the interaction of Arctic amplification with the normal range of major weather events. Examples are typhon Merbok that resulted in extensive coastal erosion in the Bering Sea, Greenland melt, and record temperatures and melt in Svalbard. The Arctic can now be considered to be in a different state to before fifteen years ago. Communities must adapt for such intermittent events to avoid worst-case scenarios.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".