Learning Multi-Sensor Confidence using Difference of Opinions
Bibliographic record
Abstract
Multiple sensors are being employed in different environments for performing various observation tasks and detecting events of interest occurring in the environment. However, all the sensors deployed in the environment do not have the same confidence level due to their differences in capabilities and imprecision in sensing. The confidence in a sensor represents the level of accuracy that it provides in accomplishing a task, which can be computed by comparing the current observation of the sensor through tedious physical investigation. Confidence computed in this manner is static and does not evolve over time. Moreover, performing physical investigation for checking the accuracy of the sensor observation is not feasible in a running system due to the overhead in incurs. Nevertheless, it is essential to know how the sensors are performing in a real-time scenario. This paper addresses this issue and proposes a novel method to dynamically compute the confidence in sensors by learning the differences of their individual opinions with respect to the particular detection task. Experimental results show the suitability of using the dynamically computed confidence as an alternative to the accuracy measures of the sensors.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 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".