Biochar and deactivated yeast as seed coatings for restoration: performance on alternative substrates
Bibliographic record
Abstract
Seedling establishment is often a critical bottleneck to revegetation of mine tailings and similar substrates with a low capacity to retain water and plant nutrients. Biochar and deactivated yeast are potential sustainable, low-cost materials with high nutrient- and water-holding capacity that could be used in this context as seed coatings to aid in seedling establishment on challenging substrates. We conducted a greenhouse study to assess the effectiveness of biochar and deactivated yeast, applied alone in a factorial combination, as seed coatings, on the germination, establishment, and early growth of white clover (Trifolium repens) and purple prairie clover (Dalea purpurea). Coated seeds were applied to a mine tailing, a coarse granitic sand, and potting soil mix substates; seedling establishment and growth were monitored over 75 days. Results indicate strong interactive effects of seed coating type with effects of species and substrate. Biochar coatings enhanced germination of Trifolium, while yeast coatings did so for Dalea. In some cases, these effects persisted throughout the experiment. Biochar coatings resulted in a ~5-fold increase in Trifolium biomass at harvest for plants in the potting soil mix but had neutral effects on sand and tailings. Yeast seed coatings enhanced biomass at harvest on tailings for Dalea. Our results indicate that biochar- and yeast-based seed coatings can have lasting effects on plant growth well beyond germination, but also emphasize highly species-specific responses that highlight the need for further study of broader patterns and mechanisms.
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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 | 0.000 | 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".