Animal Tissue Compost as a Potential Substrate Amendment for Production of Four Annual Floriculture Crops
Bibliographic record
Abstract
The objectives of this study were to determine the effectiveness of using animal tissue compost (ATC) as a substrate amendment for ornamental plant container production. The compost was produced using soiled sawdust bedding mixed with assorted animal tissues and actively composted for at least 6 months and cured for 6 to 10 months. Five substrate treatments that consisted of four different ratios of ATC and Canadian sphagnum peatmoss were formulated, all containing 20% medium grade horticultural perlite. Four species [geranium ( Pelargonium × hortorum ‘Maverick Red’), marigold ( Tagetes erecta ‘Inca II Yellow’), pansy ( Viola × wittrockiana ‘Delta Premium Yellow Blotch’), and petunia ( Petunia × hybrida ‘Prostrate Wave Purple Improved’)] were evaluated with weekly plant measurements. Geranium and petunia exhibited 100% survival for all treatments. Marigold and pansy showed 100% survival for the control treatment (0% ATC) and the treatment with the smallest amount of ATC (20% ATC). Treatments for pansy and marigold with more than 40% ATC exhibited 40% to 90% survival. All ATC substrate treatments produced the same number of flowers and buds as the control in geranium, marigold, and petunia, while the treatments containing 20% to 60% of ATC for pansy exhibited more flowers and buds than the control. Measurements of pH and electrical conductivity (EC) varied based on treatment. Based on the species and the ratios of peat, ATC, and perlite tested here, ATC has the potential to be a peat extender in floriculture substrates when used in ratios of 20% or less.
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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 | 0.000 |
| 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 | 0.001 | 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".