Evaluation of cattail fibre as a sustainable alternative to rock wool as a substrate in aeroponic systems
Bibliographic record
Abstract
Aeroponic systems offer an efficient method for growing vegetables in cold regions, like Canada’s northern and urban areas during winter. Rock wool, a commonly used growth medium, is synthetic, non-environmentally friendly, and not locally available. This study explored cattail blocks (Typha latifolia) as a sustainable alternative by comparing germination rates, plant survival, plant weights, and reusability for arugula (Eruca sativa), lettuce (Lactuca sativa), and tomato (Solanum lycopersicum). Results showed that germination was initially faster in cattail blocks than in rock wool, with significant differences (p < 0.05) across all three vegetables. Plant survival in aeroponic systems was significantly higher in cattail blocks (p < 0.05). After 35 days, fresh plant mass was higher in rock wool, but the difference was not significant for arugula, though it was significant for lettuce and tomato (p < 0.05). Rock wool degraded and developed mould, making it unsuitable for reuse, while cattail fibres retained their structural integrity. Fourier Transform Infrared (FTIR) analysis indicated nutrient loss in used cattail fibres, as peaks at 2360 cm⁻¹ and 1597 cm⁻¹ were absent. Overall, cattail fibres showed potential as an eco-friendly, locally available alternative to rock wool for aeroponic systems, supporting sustainable agriculture in cold climates.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".