Bibliographic record
Abstract
Earthquake, submarine landslides and tsunamis- their causes and effects With the recent disaster in south-east Asia, geohazards suddenly reached a high public awareness. Events like submarine earthquakes and landslides may generate destructive "tidal waves", or tsunamis, speeding toward shore. Some of the largest and most recent known landslides worldwide (the Storrega slide northwest off Norway, about 7000 years ago) and the largest documented landslide in Canada (Grand Banks slide, 1929) both occurred in the marine environment. The understanding of submarine landslides has been restricted because of our inability to observe the detailed morphology and structure of the landslide deposits and scars in the past. Modern marine geophysical survey technologies such as multibeam sonar and 3D seismic reflection are capable of providing geomorphologic data at very high resolution. The session " Earthquakes, submarine landslides and tsunamis- their causes and effects" during the 2nd JGFoS Symposium to be held in Japan will focus on actual scientific knowledge about the reasons for such geohazards, hot spots where these events most likely occur in near future, our current technological ability in prediction and forecasting
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.764 | 0.604 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".