Physical Properties of Organic Soil: Adapting Mineral Soil Concepts to Horticultural Growing Media and Histosol Characterization
Bibliographic record
Abstract
Growing media are used in a broad range of applications, for which special consideration must be given to their physical and hydraulic character. Because they are relatively fragile, dominantly consisting of dried plant remnants, their preparation, processing, and handling before potting affect their properties. This is complicated by their subsidence and decomposition during use, which leads to a reduction of their initial bulk volume. Organic growing media show many similarities to Histosols because of the common botanical origin of some of their components. For both growing media and Histosols, classical concepts and values related to physical properties like air‐filled porosity, bulk density, available water, hydraulic conductivity, gas diffusivity, and field capacity need to be adapted to reflect distinct differences in their composition, structure, and stability compared with mineral soils. Their use in containers with a variety of shapes and sizes influences water and air storage and exchange as well. They can subside extensively as they undergo decomposition. They shrink. Hence, the range of values observed for the physical properties of organic media differs from those of mineral soils. The methods to be used for measuring such properties must be adapted to that specific context of use and to account for their fragile and dynamic nature. Finally, specific norms to guide substrate manufacturing and for diagnosis of plant growth problems have been derived specifically and should be used in such a situation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".