First Insights Into INTUIT: An INteractive Tactile Physicalization for User Interpretation of RADAR Technology
Bibliographic record
Abstract
The changing climate and increasingly unpredictable sea ice conditions have created life-threatening risks for Inuit, the residents of the Arctic, who depend on the ice for transportation and livelihood. In response, they are turning to technology (e.g., RADAR imagery from the Canadian RADARSAT satellite) to augment their traditional knowledge of the ice and to map potential hazards. The difficulty lies in the actual RADAR interpretation process. In order to support understanding of the RADAR image content, we introduce a work-in-progress (WIP), INTUIT, a physicalization that represents the RADAR reflection strength, which is highly influenced by surface roughness, as a tactile texture. Such tactile texture is made by resampling the RADAR imagery to a number of UV cells and mapping the average brightness value of each cell to a physical variable. A proof of concept was designed for a region in Baffin Island (Nunavut) and sent to the Arctic for initial feedback. Preliminary study results are promising: it is expected that INTUIT will facilitate the interpretation learning process for RADAR imagery.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".