Design and Testing of Bioreceptive Porous Concrete: A New Substrate for Soilless Plant Growth
Bibliographic record
Abstract
Porous concrete with a high void content has gained popularity in urban environments as it allows water to spread through its pores. Permeability is a desired characteristic when developing media for plant growth as it enables roots to spread and anchor themselves. This leads to the aim of this study, developing a porous concrete substrate for plant growth with a pH lower than that of standard concrete. The substrate incorporates recovered industrial byproducts from blast furnaces. The materials used in the design consist of a blast furnace slag binder, two proprietary alkali activators, quartz aggregates (2.0–3.2 mm in size), a void content of 30%, and a water to binder ratio of 0.295. Tomato ( Solanum lycopersicum ), radish ( Raphanus raphanistrum ), and romaine lettuce ( Lactuva sativa ) were seeded onto the slag porous concrete for a 28-day hydroponic experiment. The treatments with porous concrete substrates differed in concentrations of the nutrient solution: Hoagland normal (1×), double Hoagland (2×), and quintuple Hoagland (5×). Rockwool with a 1× nutrient solution was selected as the control treatment, a hydroponic standard for plant growth. The dry mass values of the 2× treatment and the control treatment were similar ( P > 0.05). The largest dry mass of all treatments investigated was the radish in the 2× treatment at 125.4% of the control radish dry mass.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".