Bibliographic record
Abstract
I was intrigued to see a World Meteorological Organization (WMO) statement on extreme weather events (Eos, 15 July 2003, p. 262) issued during the peak of the northern hemisphere summer season, when parts of Europe were experiencing a heat wave and the U.S. Midwest experienced a rash of tornadoes. As a long‐term resident of eastern North America—Toronto, to be exact—I was puzzled to note that WMO did not choose to issue such an “extreme weather statement” during the last winter season, January–March 2003, when eastern North America experienced one of the longest and coldest winters in many years. Several communities in the Canadian Atlantic provinces experienced extreme cold spells and record‐breaking snowfall amounts. In the interior region of Newfoundland, several communities were cut off for a few weeks because of ice jams due to frozen rivers and lakes. Further south, along the U.S. Atlantic seaboard, heavy snowfall amounts were recorded with some locales in the Washington‐Baltimore area receiving up to 100 cm of snow in 24 hours! The unusually long and cold winter of 2003 was felt as far south as Bangladesh and Vietnam, where several hundred people died of exposure during a month‐long cold spell. In northern Europe, the Gulf of Finland between Scandinavia and Russia was frozen over for the first time since 1947!
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.037 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".