Keyhole, Google Earth, and 3D Worlds: An Interview with Avi Bar-Zeev
Bibliographic record
Abstract
Avi Bar-Zeev is a co-founder of Keyhole () – maker of EarthViewer, which later became Google Earth ( http://earth.google.com ) – and an early employee of Intrinsic Graphics and a number of interesting start-ups. He developed technologies for Second Life, including the procedural 3D object rendering code. Early in his career, he helped develop Disney's Aladdin's Magic Carpet VR Ride, ( http://www.imagineering.org/wdilabs.html ), one of the first real-time (60 fps) first-person immersive entertainment applications, and went on to lead or influence a number of Disney 3D experiences. He typically consults for a living, inventing technologies as needed and helping clients through the maze of options. He regularly blogs about subjects related to his technical interests and expertise at http://www.realityprime.com .
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".