Extracting Natural Colors from Marigolds for Watercolor Painting in Interior Architecture
Bibliographic record
Abstract
Color is one of the essential factors in daily life, and humans have used natural pigments since prehistoric times, derived from locally available natural materials.However, synthetic dyes produced industrially have been developed to replace natural pigments and have gained popularity, leading to the decline of natural pigment usage.Flowers are widely available natural materials that provide pigments.Marigolds are a popular flower among Thai people because they are easy to grow and cultivate.They can be cultivated year-round and produce vibrant, long-lasting flowers.In the Vishnukarman offering ceremony, a significant number of marigolds are left over after worship.The research team extracted natural colors from marigolds by hot extraction or boiling to develop watercolors.Experiments have shown that marigolds can be used to extract pigments through boiling.The natural pigment extraction from marigolds was conducted through hot extraction at 140 for 30 minutes using three methods to compare the resulting yellow intensity.It consists of 1) whole marigolds, 2) fine marigolds, and 3) yellow petals.It was found that method 3 gave the most vivid yellow water, and the water color on the paper was brighter.Method 3 was further tested through repeated boiling at 140 in three sessions of three minutes each.It was found that the first boil gave the most clear colored water, and when the next boil it was more turbid, it became more turbid respectively.In conclusion, natural pigment extraction from marigolds through hot extraction can effectively produce watercolor paint.Using only yellow petals boiled at 140 for three minutes yields a bright yellow pigment.This method allows individuals to create their own watercolor paint from natural materials without relying on commercial products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".