layerlab: A computational toolbox for layered materials
Bibliographic record
Abstract
course Share on Physically based shading in theory and practice Authors: Stephen Hill Ubisoft Montreal Ubisoft MontrealView Profile , Stephen McAuley Ubisoft Montreal Ubisoft MontrealView Profile , Brent Burley Walt Disney Animation Studios Walt Disney Animation StudiosView Profile , Danny Chan Sledgehammer Games Sledgehammer GamesView Profile , Luca Fascione Weta Digital Weta DigitalView Profile , Michał Iwanicki Activision ActivisionView Profile , Naty Hoffman 2K 2KView Profile , Wenzel Jakob ETH Zürich ETH ZürichView Profile , David Neubelt Ready at Dawn Studios Ready at Dawn StudiosView Profile , Angelo Pesce Activision ActivisionView Profile , Matt Pettineo Ready at Dawn Studios Ready at Dawn StudiosView Profile Authors Info & Claims SIGGRAPH '15: ACM SIGGRAPH 2015 CoursesJuly 2015 Article No.: 22Pages 1–8https://doi.org/10.1145/2776880.2787670Published:30 July 2015Publication History 9citation1,300DownloadsMetricsTotal Citations9Total Downloads1,300Last 12 Months23Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.247 | 0.086 |
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".