Bibliographic record
Abstract
This paper discusses how colour is used in experimental fl uid fl ow studies.Firstly, colour (defi ned scientifi cally by the wavelength of the light) can be used to discriminate between different channels in optical instrumentation, making it possible to measure several fl ow properties, simultaneously, and without any intrusion into the fl ow.These aspects of modern fl ow measurement are discussed in terms of some examples.Alternatively, the results of the fl ow measurement may be represented in the form of images, graphs and three-dimensional schematics with colour being used to identify and emphasise particular features that might otherwise remain concealed.The experimental results presented in the paper confi rm that using colour to discriminate between sets of data has several important advantages over the older black and white or grey scale forms of representation.Not only do colours provide an excellent basis for communication through graphical and three-dimensional presentations, but the resulting clarity may also help to reveal complex forms of fl uid fl ow behaviour.Moreover, the inherent artistic appeal of some colour representations can draw the viewer into the technical detail, helping to simplify physical phenomena in ways that would not otherwise be possible.Although the observations made on the presentation of data are illustrated with reference to a number of experimental fl uid fl ow problems, many of the methods and observations would be applicable in other situations, where scientifi c or technological data needs to be presented.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".