Growth and Nutrient Status of Six Species of Nursery Stock Grown in a Compost-based Medium with Recycled Nutrients
Bibliographic record
Abstract
Rooted cuttings of `Antonovka' apple, `Lynwood Gold' forsythia, double-flowered kerria, common ninebark, `Goldfinger' potentilla, and `Red Prince' weigela were grown in 2-gal (6-L) nursery containers filled with 1:1 (by volume) of waste compost and composted pine bark, under three fertilizer regimes: 1) liquid nutrients [target concentrations in ppm (mg . L -1 ): NH 4 -N, 13; NO 3 -N, 100; P, 28; K, 120; Ca, 92; Mg, 13; Fe, 1.3; Mn, 0.27; Zn, 0.23; Cu, 0.05; B, 0.22; Mo, 0.05; Na, <50; Cl, <50; and SO 4 <300] delivered and recycled twice per day via a computer-controlled multifertilizer injector; 2) same nutrient formula and concentration rate delivered fresh via the injector but without recycling; and 3) Nutryon (Polyon) 17-5-12 controlled-release fertilizer incorporated into the medium at a rate of 11 lb/yd 3 (6.5 kg·m -3 ). With recycled liquid nutrients, all species grew the same or more than with nonrecycled nutrients, and generally the poorest growth was with controlled-release fertilizer. Foliar concentrations of K (all species), N (all species), P (forsythia, kerria, potentilla, and weigela), and Mn (forsythia, potentilla, and weigela) were higher in plants supplied with recycled and/or nonrecycled nutrients than in those supplied with controlled-release fertilizer, while foliar concentrations of Ca (ninebark and kerria) and Mg (apple, kerria, ninebark, potentilla, and weigela) were lower. Compared to nonrecycled liquid nutrients, the amounts of individual recycled nutrients were reduced by (percentage in brackets): NH 4 -N (30), NO 3 -N (78), P (76), K (46), Ca (93), Mg (96), Fe (52), Mn (43), Zn (55), Cu (60), B (83), and Mo (66).
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".