INFLUENCE OF DIFFERENT RATES OF BIOCHAR AND DEER BROWSING ON POLLINATOR PLANT PRODUCTIVITY
Bibliographic record
Abstract
Biochar, a carbon-rich material produced from biomass, has received wide attention for soil amendment to improve plant growth. However, its specific effects on pollinator friendly plants remain poorly understood. In this study, we evaluated the effects of four biochar application rates (0, 10, 20, 40 tons/acre) and deer exclusion (fenced vs. unfenced) on the productivity of three pollinator species; Buckwheat (Fagopyrum esculentum), Plains Coreopsis (Coreopsis tinctoria), and Black-eyed Susan (Rudbeckia hirta) in a utility right of way with a history of mining. A split-plot randomized design was used, and the results were analyzed using linear mixed-effects models. Fencing had a significant effect on height for both buckwheat and plains coreopsis (p < 0.0001), while biochar showed no statistically significant effect on growth in any species. However, the tallest plants were observed in fenced plots with 10–20 tons/acre biochar. Black -eyed Susan showed minimal response to either treatment across both measurements (p > 0.05). One-way ANOVA results revealed that biochar application significantly influenced soil pH, SOM, and potassium (K) levels (p < 0.05), suggesting biochar’s potential to enhance soil fertility even when short-term growth responses are limited. Findings from this study suggest that although biochar can enhance soil qualities, successful restoration of pollinator plants also requires protection from deer browsing. Integrating soil amendments with fences may be critical for enhancing the success of pollinator plantings.
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 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".