Gas diffusion and air-filled porosity: Effect of some oversize fragments in growing media
Bibliographic record
Abstract
Large particles are often added to growing media to improve air-filled porosity, an often-reported growth-limiting factor. Previous studies have shown that large fragments can leave air-filled porosity unchanged and actually decrease the air exchange characteristics in peat:bark and pure peat media, with an adverse effect on plant growth. Thirteen different growing mixes composed of perlite, bark, peat, sand, rockwool, wood and coco fibre, in which the size of the larger fractions was varied, were tested. Air-filled porosity, easily available water, pore effectiveness coefficient, and diffusivity were examined in these mixes. Air-filled porosity was found to be the highest in mixes where the largest fraction was composed of coarser particles. Pore effectiveness coefficient was found to decrease with the increasing coarseness of the largest fractions. Gas diffusivity was found to be highest when intermediate (2–4 mm) fragments were used as the largest fraction of the mixes. These findings applied to impermeable, disk- or spherically shaped, coarse fragments (perlite, coco hulls, bark), but did not apply to threadlike coarse material (wood fibres). Gas diffusivity values (at a water potential of −0.75 kPa) obtained for rockwool, the peat:bark mix containing 2- to 4-mm bark particles, the peat:medium wood fibre mix, the peat:fine perlite (0.8–2 mm) mix and the pure fine coconut medium were not statistically different one from another. Air-filled porosity was negatively correlated to the fractions less than 1 mm, between 0 and 0.2 mm and between 0.2 and 0.8 mm. Easily available water, pore effectiveness coefficient and gas diffusivity were not correlated to particle size distribution, indicating that these properties cannot be estimated based on particle size and must instead be measured directly. Key words: Aetion, peat substrates, perlite, bark, available water
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".