Impact of Sensor Noise Magnitude on Emergent Features of Ecological Interface Design
Bibliographic record
Abstract
This paper describes a study on the impact of sensor noise magnitude on the emergent features of an Ecological Interface Design (EID) interface using a representative thermal-hydraulic process simulation. Previous studies conducted by St-Cyr and Vicente (2004, 2005) showed no difference between EID and Single-Sensor Single-Indicator (SSSI) interfaces when the magnitude of sensor noise was globally increased to all sensors. However, to date, no study investigated the impact of gradually increasing sensor noise magnitude to selected sensors that are used to derive emergent features portrayed on EID interfaces. The current study filled part of this gap by locally increasing the magnitude of sensor noise. Results show that performance of EID group decreased, while performance of the SSSI group did not. However, the performance of EID participants was not inferior to that of SSSI participants. This is explained by the fact that participants in the EID condition had to deal with distorted emergent features.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".