Application of Paper Mill Biosolids, Wood Ash and Ground Bark on Wild Lowbush Blueberry Production
Bibliographic record
Abstract
SUMMARY Soils in wild lowbush blueberry production are prone to wind erosion and have very low nutrient and water storage capacities. An experiment was initiated to assess paper mill biosolids (PB) mixed with wood ash and ground bark as a soil amendment/fertilizer for wild lowbush blueberry (Vaccium angustifolium Ait.) in the Lac St-Jean area, Quebec, Canada. A mixture of PB was applied during spring (mid-May) of the sprout year (1998) on 120 m2 plots at a rate of 15t ha−1 (wet basis) with wood ash (1 and 2t ha−1) and ground bark (0, 3, 6,9 and 15t ha−1, wet basis). Blueberry leaves were sampled in the first year and wet digestion and dry ashing were performed to determine foliar nutrient concentration. In 1999 and 2000, fruit yields tended to increase with PB with wood ash and ground bark application (31% in 1999 and 29% in 2000). Foliar N, P and K concentrations were increased whereas Ca and Mg were unaffected compared to control. Other nutrients were also determined and only Fe tended to increase with PB application whereas Ni tended to decrease. This study indicated that PB mixed with wood ash and ground bark is a potential nutrient source for blueberry on these poor sandy soils without short-term loss in crop yield.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".