Bibliographic record
Abstract
Abstract The ICC profile format specifies the widely used ICC color profile for transforming color data between different devices and color spaces. Ambiguities in the previous version were resolved in the v4 specification, which also introduced a perceptual reference medium to provide a well‐defined intermediate gamut as a target for gamut mapping and re‐rendering between source and destination data. Since the first publication of the v4 specification, there have been a number of important amendments, which collectively move the ICC color management architecture further away from its original static processing model to a more dynamic and flexibly programmable one. With the new colorimetric intent image state tag, it is possible to identify transforms which are suitable for images in an input‐referred image state and which need to be processed accordingly. ICC also provides an on‐line profile registry now, a permanent repository of profiles for standard printing conditions. The registry supports both manual selection and automated download of profiles. The floating‐point device encoding range amendment introduces support for floating point data, but also introduces a new and potentially more extensible form of color transform, known as multiprocessing elements. In the future these may be extended to provide a range of color processing capabilities that are not currently available. This is an important component of the smart and programmable CMM concept, in which the color matching module uses data in the profile as well as rules and other information to construct the most suitable transform for the image, workflow, and user preference. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 444–448, 2008
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".