Wool and Hair Waste as Nutrient Source for High-value Crops
Bibliographic record
Abstract
One small-plotfield and five container experiments were conducted to evaluate sheep wool-wastes and human hair-wastes as soil amendments and nutrient sources for high-value crops. Overall, the wool-waste or hair-waste addition to soil: 1) increased yields from basil, garden sage, peppermint, valerian, thorn apple, marigold, foxglove, and swiss chard; 2) increased the amount of secondary metabolites (such as essential oils and alkaloids); 3) increased NH 4 -N and NO 3 -N in soil; 4) increased total N (and protein) content in plant tissue; 5) did not affect soil microbial biomass; and 6) decreased mycorrhizae colonization of plant roots. Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analyses indicated that some of the wool and hair in soil from the container and field experiments (after two field seasons and several harvests) retained its original structure, retained a significant amount of S and some N, and was not fully decomposed. Our results indicate that single addition of wool or hair-waste of 0.33% by weight to soil would support two to five harvests or crops, without addition of other fertilizers, and may improve soil biological and chemical characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".