Bibliographic record
Abstract
Figure 1. The Birch Glacier in the Swiss Alps collapsed on 28 May 2025, causing a massive landslide which devastated the village of Blatten in the Lötschental valley. This image taken by the Copernicus Sentinel-2 satellite on 30 May 2025 shows the extent of the area that was affected. The area of brownish-grey in the image shows the path of the landslide. Around 3 million m3 of debris crashed down the slope, destroying almost 90% of the village. (Image credit: European Union, Copernicus Sentinel-2 imagery.) Figure 2. Numerous large wildfires were burning across Canada at the end of May 2025. The smoke from the wildfires can be seen as far south as southeastern parts of the USA in this satellite image. Persistent drought over the past few years, combined with unusually hot conditions that were made more likely by climate change, caused these wildfires to start so early in the season. The wildfire season in Canada typically runs from April through September or October. This NOAA-20/VIIRS satellite image was taken on 31 May 2025. (Image credit: NASA NOAA-20/VIIRS.)
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.417 | 0.274 |
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".