Glacier and Permafrost Problems in High South Asia: International Workshop on Cryosphere and Hazards for Hindu Kush, Himalayas and Tibetan Plateau; Kathmandu, Nepal, 31 March to 2 April 2008
Bibliographic record
Abstract
An outgrowth of prior conferences that had to be held separately for political reasons [Bagla, 2006a, 2006b], a 3‐day workshop to discuss the impact of climate change on the glaciers and permafrost of high Asia hosted some 70 geoscientists from China, India, Nepal, Pakistan, and the United States and a few from Europe and Canada. The scientists concluded that a major need exists for better long‐term monitoring of glaciers in the region using satellite imagery coupled with direct observations in the field. Also emphasized was the lack of cooperation or lack of sharing of essential hydrological information between the commonly militarily opposed countries in the area. Even within each country, agencies do not necessarily share data with other agencies or with university scientists. For example, a ludicrous situation was noted wherein it is easier to obtain high‐resolution imagery and digital elevation models from scientists in opposing countries rather than domestically, where such data can be restricted or illegal to use. Such data hoarding potentially increases a population's risk because people who are trying to forecast hazardous droughts, floods, and landslides commonly cannot get appropriate information.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".