Bibliographic record
Abstract
The proliferation of wireless sensors in everyday consumer products presents new opportunities to monitor the environment at unprecedented space and time scales. This study explores the utility of pervasive sensors for improving the resolution of areal precipitation estimates through fusion with weather radar observations. While these sensors are not specifically designed to measure rainfall intensities, the data they collect can be repurposed to provide quantitative measurements of environmental variables at the location of the sensor. Due to different measurement accuracies (which may be time dependent), types of spatial and/or temporal measurement support, and measurement frequencies of the component sensors, it is unclear how best to combine measurements from pervasive sensors with those from traditional sensors. The method developed in this study employs Markov random field models to compute the likelihood of rainfall at sub-grid pixels. These likelihoods are used to "unmix" the block-averaged rainfall rate measured by the radar. The statistical nature of the model permits the data evidence to drive the fusion of the sensors' measurements. The performance of these methods will be illustrated using case studies exploring synthetic and real-world data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".