Harmonisoitu luonnonuhkien varoitusjärjestelmä
Bibliographic record
Abstract
Natural hazard means a natural process or phenomenon which poses a threat from the human perspective. The term may refer both to an actual event or the latent conditions with the potential of causing future events. Natural hazards may lead to natural disasters. A natural disaster takes place on average more than once per day and hundreds of millions of people are affected by them every year. Floods, earthquakes and storms are among the most destructive phenomena. Exposure to a natural hazard is only one reason to a natural disaster. Other reasons include the present conditions of vulnerability. Many national and international organizations have recognized the importance of the social factor and developed systems for issuing warnings about natural hazards in order to decrease the vulnerability to hazards. Unfortunately, these systems do not currently share a standard which would define the format and the delivery method for the warnings. Because of this, it is challenging to develop a system which could be used for receiving warning information anywhere in the world. This Thesis introduces a harmonization layer, which analyzes the original warnings and makes them available in a uniform format through a web service. This makes the warning information easily accessible, provided that the local system is integrated to the service. A generic abstraction of a warning, capable of characterizing warnings originating from various warning systems, is presented. Based on the abstraction, a data structure for storing static and dynamic information about the warnings is designed. As an example of acquiring and harmonizing original warnings and keeping the data structure up to date, a script for managing the warnings issued by the Meteorological Service of Canada is presented. Access to the data structure is integrated to an existing web service. The Thesis discusses extending its interface and provides guidelines for interpreting and visualizing the responses. Finally, a visual tool for monitoring the performance of the web service and the quality of the harmonized data against the original data is developed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.056 |
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; both teacher heads 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".