Visualizing perceived spatial data quality of 3D objects within virtual globes
Bibliographic record
Abstract
Virtual globes (VGs) allow Internet users to view geographic data of heterogeneous quality created by other users. This article presents a new approach for collecting and visualizing information about the perceived quality of 3D data in VGs. It aims at improving users' awareness of the quality of 3D objects. Instead of relying on the existing metadata or on formal accuracy assessments that are often impossible in practice, we propose a crowd-sourced quality recommender system based on the five-star visualization method successful in other types of Web applications. Four alternative five-star visualizations were implemented in a Google Earth-based prototype and tested through a formal user evaluation. These tests helped identifying the most effective method for a 3D environment. Results indicate that while most websites use a visualization approach that shows a ‘number of stars’, this method was the least preferred by participants. Instead, participants ranked the ‘number within a star’ method highest as i...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".