Bibliographic record
Abstract
New environmental extremes are currently underway and are much greater than those in previous records. These are mostly regional, singular events that are caused by global change/local weather combinations and are larger than the impact of linear temperature increases projected using climate models. These new states cannot easily be assigned probabilities because they often have no historical analogs. Thus, the term super climate extremes is used. Examples are the loss of sea ice and ecosystem reorganization in northern marine Alaska, heatwave extreme in western Canada, and the loss of snow in Greenland. New combined extreme occurrences, which are reported almost daily, lead to a new, higher level of climate change urgency. The loss of sea ice in 2018–2019 was a result of warmer Arctic temperatures and changes in the jet stream. They resulted in a chain of impacts from southerly winds, the northward movement of predatory fish, and the reduction of food security for coastal communities. Record temperatures were measured in southwestern British Columbia following previous drought conditions, a confluence of two storm tracks, and warming through atmospheric subsidence. Greenland’s losses had clear skies and jet stream events. Such new extremes are present indicators of climate change. Their impacts result from the interaction between physical and ecological processes, and they justify the creation of a new climate change category based on super climate extremes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".