Measuring the color of granite rocks: A proposed procedure
Bibliographic record
Abstract
Abstract In spite of color being one of the physicochemical parameters most commonly used to characterize ornamental stone, there is yet no standardized protocol for measuring this parameter. Such a protocol is of particular importance for characterizing the color of heterogeneous surfaces, as in the case of granite. The aim of the present study was to determine the minimum area and the number of measurements required to characterize the color of granite rocks. A spectrophotometer and a tristimulus colorimeter, were used to measure the color of granite samples, and the measurements were expressed in CIE L*a*b* color system units. Three parameters were considered as variable factors: the type of rock (Labrador Claro, Grissal, Rosa Porriño, and Blanco Cristal), surface finish (polished, honed, sawn, and flamed), and target area (circular apertures of diameter 5, 8, 10, and 50 mm). The results of the application of multivariate analysis of variance and of the classical CIELAB formula and CIE L*a*b*‐based color‐difference formulae (i.e., CIE94 and CIEDE2000) to the data revealed that, although all considered factors affected the minimal area and the number of measurements required, the different circular apertures of both the instruments can be disregarded if the number of measurements and area recommended in this study are used. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".