Relationship of Soluble Salts Content in MSW Compost Media and Rooting of Evergreen Cuttings
Bibliographic record
Abstract
Municipal solid waste (MSW) compost media with various levels of soluble salts were used for rooting stem cuttings of nine evergreen landscape shrubs: Buxus sempervirens L. ‘Green Gem’; Juniperus chinensis L. ‘Hetzii; J. chinensis ‘Mint Julep’; J. chinensis ‘Pfitzeriana Aurea’; J. horizontalis Moench ‘Bar Harbor’; J. horizontalis ‘Blue Chip’; J. sabina L. ‘Blue Danube’; Taxus x media Rehd. ‘Densiformis’; and Thuja occidentalis L. Rooting occurred during the winter in greenhouse compartments supplied with bottom-heated benches filled with 100% sphagnum peat or 100% perlite, or peat or perlite mixed with 15, 30, 45, 60 or 75% by volume of MSW compost. The electrical conductivity (salt) levels were similar in MSW compost with peat or with perlite (range, 0.05-0.60 dS·m−1 with 0-75% compost) and positively correlated with levels of MSW (r = 0.88, P≤0.001). With few exceptions, cuttings rooted similarly in MSW with peat or perlite. Depending on taxa, increasing salt levels had various degrees of diminutive, neutral, and enhancing effect on rooting response, expressed in terms of percent rooting, root number per cutting, and root length (longest root per cutting). Four taxa (J. horizontalis ‘Bar Harbor’ and ‘Blue Chip’, J. sabina ‘Blue Danube’, and T. occidentalis) were tolerant of the salt levels tested (positively influenced or unaffected). The other five taxa were intolerant (adversely affected).
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 | 0.000 | 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".