Bibliographic record
Abstract
Real world materials exhibit characteristic surface reflectance, such as glossy or specular highlights, and anisotropy that need to be modeled accurately for realistic rendering applications. The surface reflectance of a material is formalized by the notion of the Bidirectional Reflectance Distribution Function (BRDF). The acquisition of real world BRDF data, particularly with image-based techniques, has been a very active area of research over the last few years. Independent of the acquisition process, the acquired data is generally not used directly due to its large size, the noise present in the measurement process, and missing data for certain incident and exitant directions. Instead, the data is usually either fitted to an analytical model or projected into a suitable basis as a post-process. Recently, Ghosh et al. [2007] have proposed an alternative approach where the BRDF data is optically projected into a suitable basis function directly during the capture process. This speeds up acquisition time to one or two minutes compared to a few hours required by traditional approaches. They develop a set of basis functions for this purpose that are similar to the spherical harmonics basis and are orthonormal over the zone of directions that can be simultaneously covered with their optical setup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".