Physical Properties of Organic and Inorganic Substrates Distributed in Domestic Market for Hydroponic Cultivation of Strawberry
Bibliographic record
Abstract
This study was carried out to investigate the aging effects of coir dust (CD) and different origins of peat mosses (PM) on root substrate physical properties.The blending effects of CD or PM with various ratios of vermiculite (VL) or perlite (PL) on the changes in physical properties were also investigated.The physical properties of aged coir dust (ACD) compared to fresh coir dust (FCD) showed no significant differences in total porosity.But, container capacity increased from 59.6% to 71.1%, and air-filled porosity decreased from 30.1% to 18.9%.The total porosity of imported PM was, 83.8% in Estonia, 82.6% in Canada, 82.5% in Latvia, and 81.8% in Lithuania.The container capacity of Lithuanian PM was the highest with 75% followed by 73.1% in Canada, 71.8% in Latvia, 71.2% in Estonia, but the air-filled porosities were 12.5% in Estonia, 10.7% in Latvia, 9.50% in Canada, and 6.90% in Lithuania.When the mixing rate of vermiculite to ACD or FCD were elevated, the total porosity was reduced and the elevation in ACD resulted in the quadratic decrease of air-filled porosity (R 2 = 0.6127, p ≤ 0.01).By increasing the mixing ratio of perlite to ACD or FCD resulted in the decrease of total porosity.Similarly, by increasing the quantity in mixing ratio of pearlite to ACD, decreased the container capacity with the quadratic tendency (R 2 = 0.5687, p ≤ 0.01).The elevation of mixing ratios of PL or VL influenced differently on total porosity, air-filled porosity, and container capacity in each of the imported PM.This is due to the particle size.In summary, ACD is more suitable for hydroponic cultivation of strawberry due to its lower air-filled porosity and higher container capacity compared to FCD.It is also better to increase the air-filled porosity to 15% or more by mixing PM with PL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".