Statistical Method for Determining the Levelness Parameters of Different Coloured Polymeric Fabrics
Bibliographic record
Abstract
In this research work an objective method for evaluating the levelness (L) of coloured textile materials by spectrophotometric measurements is reported. Colour levelness is actually a description of the uniformity of colour shade in different places of the fabric. Thus, we use three different fabrics namely, wool, polyester and wool/polyester blend (65/35%), firstly these fabrics are treated with different uv/ozone exposure times continued for one hour exposure, followed by dyeing separately with three different dye classes; reactive, direct and acid. The colour strength (K/S) and colour difference ?E after exposing of the treated dyed samples to artificial day light are measured spectrophoto-metrically. The results of these measurements are statistically correlated with the levelness (L), relative standard deviation Sr (?), and the unlevelness (U). The obtained results showed that the dyeability, L parameter, U parameter and ?E values are greatly depend on the dye class used, fabric nature, and type of treatment applied. Where as these parameters reflect the effect of homogeneity of dye distribution on the fabric which decreases the colour fading of the dyed samples under test. Where uv/ozone exposure leads to the increase in the amorphousity of the exposed samples especially at the end of exposure leading to an increase in the exhaustion and diffusion of the dye inside the fabric pores besides, to its effect on districting the dye accumulation and hence increases the levelness of dyeing.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".