Bibliographic record
Abstract
Nepal Divested The series of earthquakes of around 7.8 magnitudes rumbled the entire city to wreckage with a distinct sense of déjà vu. An irreparable damage has been done. Countless death and injuries, millions of people rendered homeless. The beautiful valleys in the country obliterated. In the matter of the seconds, the “Land of Temples” lies quite in rubbles. The entire World is yet to come out of this stagger. With the speculations of more such strikes, a worldwide alert is declared. Not long back Japan faced almost the same story with the earthquake of magnitude 9.0 triggering tsunami, killing some 27, 500 people followed by a Nuclear catastrophe in the northeast Japan. The most devastating happening of the times after the Second World War pushed Japan back to the times of their past wreckage. Japan took hundreds of years to come out of that rubble and found itself in the same soup. With radiations levels soaring in the seawater near Fukushima environmentalists felt the ripples of it across globe. Global Warming has become the most talked about issue. On the 45th Anniversary of Earth Day, its imperative to open a serious discourse about the coverage of environment issues by the media.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".