Retrospective and Prospects for Cold Regions Landslide Research (2012–2016) (WCoE 2014–2017, IPL-132, IPL-167, IPL-203, CRLN)
Bibliographic record
Abstract
For nearly 100 years, the average temperature of the global surface has showed a consistent warming trend. Climate change and extreme weather events causing landslides are rising, especially landslides in cold regions, and the topic has become a hot issue in landslide research. With the support of ICL and the Chinese government, based on highway construction projects in Heilongjiang Province (China), Prof. Shan and his group (Institute of Cold Regions Science and Engineering, North East Forestry University, China) conducted thematic studies focusing on environmental and engineering geology problems in cold regions in the context of climate change, such as IPL132, IPL167, and IPL203. These studies attracted the interest of international colleagues, then Chinese colleagues, together with researchers from Russia, Canada, Japan, Italy and Czech Republic, together organized the ICL-cold regions landslide network (ICL-CRLN). In ICL-CRLN researchers could exchange research information and results, and so promote the development of landslide research in cold regions. In 2014, IPL-GPC approved the establishment of IPL-WCoE: Research Center of Cold Regions Landslide, so landslide research in cold regions came into a new stage of development. This article is a summary and outlook of these activities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".