Impact of biochar and industrial ash amendments on soil properties, growth and nutrition of black and white spruce seedlings in a sandy loam soil / by Robin Sevean
Bibliographic record
Abstract
"The purpose of this study was to establish and examine a controlled field experiment near Thunder Bay, Ontario using industrially produced ash and biochar as a soil amendment. This study monitors the change in physical, chemical, and biological properties to the field soil, as well as, the growth of black and white spruce seedlings. Biochar and ash were applied to split plots (black spruce on one half and white spruce on the other) at the levels of 0, 1, and 10 tonnes ha-1. Ash application at 10 tonnes ha-1 caused the most significant changes to the soil?s chemical properties including: increasing pH, electrical conductivity, Ca, K, Na, estimated cation exchange capacity, S, and Zn; while decreasing Mg, and available/mineralizable NH4. The only significant change to the soil from biochar application was a decrease in extractable Cu concentrations after the application of 10 tonnes ha-1. There were no significant differences between treatments in tree growth after two growing seasons. However, seedling foliage nutrient concentrations increased significantly for some nutrients with the application of ash. Black spruce and white spruce both increased in foliage nutrients B, K, and S. However, only black spruce seedling increased in foliar Ca, and Mg, which was likely due to a difference in rooting patterns. It is possible that since the plots were located on an old nursery site that most nutrient deficiencies have been amended in the past and the effects of the treatment on the soil were not as great as they could be on poorer soil. The increase in foliage nutrient concentrations in black and white spruce points to possible changes to seedling growth in the future. Therefore, a more long term study must be done to determine if seedling performance will be affected by these treatments."-- from abstract.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".