Evaluating the biocompatibility of ceramic materials for constructing artificial reefs
Bibliographic record
Abstract
Introduction Coastal ecosystems, including reefs, are becoming increasingly threatened as anthropogenic development continues to encroach on intertidal habitats with little initiative to establish ecologically considerate infrastructure. Submerging human-made, shelter-providing structures known as artificial reefs (AR) can contribute to the preservation of these ecosystems. ARs are historically used for promoting the abundance and biodiversity of marine species for aquaculture, conservation, and ecotourism; and are typically made of concretes or metal structures. An AR’s success correlates to its ability to establish a surface layer of microorganisms, such as microalgae and bacteria, known as a biofilm. The productivity of the biofilm can be influenced by material surface properties. It is hypothesized that material pH and porosity affect the rate of biofilm formation. Methods Here - a range of concrete mixtures were cast and submerged in circulating seawater and mass per surface area of biofilm accumulation was measured to evaluate this theory. These mixtures included standard Portland Cement (PC), PC with admixtures of diatomaceous earth (PDC) and limestone (PLC), fine-aggregate high-performance concrete (DUC), and terra cotta (TER). ARs were manufactured as 38mm tall cylinders, 76mm in diameter, and submerged in circulating seawater to evaluate mass per surface area of biofilm accumulation. Results Our results indicate that biofilm formation is directly affected by surface porosity and less-so by pH, as determined by measuring material properties after submersion. We found that the PDC samples were most successful in forming a biofilm despite being more fragile than other concrete samples. Discussion This preliminary study provides insight into how different material properties influence the accumulation of biofilm as a starting point for designing ARs. Future work will investigate the long-term performance of such samples in relevant conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".