Defining Critical Capillary Rise Properties for Growing Media in Nurseries
Bibliographic record
Abstract
Water availability for landscape nursery irrigation is foreseen as a major impediment for this industry within the next decade. Among various solutions proposed to increase irrigation efficiency, thereby reducing the water volumes required, are closed and semi‐closed subirrigation systems designed to grow plants potted in organic growing media. These systems, however, require organic substrates that have good capillary properties. However, standards for such capillary properties are not available. This study compared substrates composed of peat, bark, and sand having contrasting capillary properties, in a nursery experiment to establish guideline values for the proper and efficient operation on capillary mat devices. It also proposes a theoretical model of capillary rise using the hydraulic characteristics of growing media to predict the suitability of various substrates. Substrates with 60% (per volume) sphagnum peat were found to provide the best capillary rise and best growth, based on empirical measurements, relative to substrates with 30% sphagnum or 30% sedge peat. The proposed theoretical model concurred with these observations.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".