Bibliographic record
Abstract
In this article some of the disasters associated to the presence of the debris flows are described briefly happened in some countries of the world in recent years, which have been observed and, in some occasions, measures its fundamental characteristics, for example: The United States, China, Japan, Italy, Taiwan, Central Asia, Germany, Switzerland, Russia, the Philippines, the Ukraine, Canada, Brazil, Ecuador and Venezuela. In the country of Mexico have appeared great amount of debris flows in the recent years, many of them in the Mexican trans-volcanic axis and coastal mountain ranges, such as the happened ones in Popocatepetl volcano, the volcano Pico de Orizaba, the volcano Nevado de Toluca, mountains of Puebla, in Acapulco, Gro., in the city of Tijuana, B.C. and in the coastal mountain range of Chiapas and Oaxaca, to only mention those that have produced important disasters in recent times. In the article the characteristics of the disasters produced in different regions from Mexico are also described. To identify the mechanisms that they involve to the solid material within a liquid flow, it is necessary to analyze the roles played by the several operating forces in the genesis of the movement in a debris flow. The developed approaches to date are the following ones: model of stability of infinite slope, approach of Shields, approach of Takahashi and recent developments. In order to be able to apply the previous models it is necessary to have very great information of diverse points on the land, with which a calculation mesh forms, therefore is essential to develop a GIS in the analysis site. A model appears in the complete article to determine the characteristics in the zone of beginning of a debris flow, which includes the development of a SIG.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".