Sensors Tell More than They Sense: Modeling and Reasoning about Sensor Observations for Understanding Weather Events
Bibliographic record
Abstract
In this paper, we argue that sensors provide a better understanding of geographic events. They produce observations that reflect the natural events taking place at a particular location. The essential part of deriving information about geographic events from sensor observations is to formalize the relations between them. In this spirit, we develop an ontology to capture the relations between weather events and properties observed by sensors. A case study is investigated to illustrate how blizzard events can be formally represented in relation to a set of atmospheric properties observed by a weather station. Using the ontological structures, we define and implement rules to reason about blizzard events from hourly weather observations. We use the historical weather records from the Canadian Climate Archives database to evaluate our approach. The result includes an interactive timeline illustrating the events. The approach is evaluated in terms of reasoning and querying support against a local use. Keywords: Events, observations, ontology, query, rule-based representation and reasoning, sensors, weather, Open Geospatial Consortium (OGC), Semantic Web for Earth and Environmental Terminology (SWEET), Descriptive Ontology for Linguistic, Cognitive Engineering (DOLCE)
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".