Growth Analysis of Chlorella vulgaris on Ceramic Tiles Using Vegetation Indices and Color Kinetics
Bibliographic record
Abstract
Ceramic substrates offer a low-cost, resourceconscious solution to enhance microalgal proliferation, diverging from traditional cultivation systems. This study investigates the growth of Chlorella vulgaris (C. vulgaris) on ceramic surfaces with three unique topographies-hemispherical indentations, linear grooves, and sawtooth-like ridges-under nutrient-rich and nutrient-deprived conditions. Proliferation was analyzed through images in the visible spectrum, with chlorophyll intensity quantified through four vegetation indices (VIs). Color kinetic models were developed using both VIs and CIELAB color parameters ($L^{*}, a^{*}, b^{*}$) and evaluated using Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC). Results showed that nutrient and moisture availability improved C. vulgaris growth by $20 \%$ in the first five days, though moisture could not be retained beyond 24 hours in open conditions. Surface textures significantly influenced biomass proliferation, with hemispherical depressions showing the highest cell attachment. The Exponential model demonstrated superior predictive performance in $57.14 \%$ of parameter-texture combinations, based on AIC and RMSE metrics. These findings highlight the potential for cultivating C. vulgaris in nonlaboratory settings using ceramic substrates, provided consistent moisture and nutrient supply is maintained.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".