Soil Nutrient Availability, Plant Nutrient Uptake, and Wild Blueberry (<i>Vaccinium angustifolium</i>Ait.) Yield in Response to N-Viro Biosolids and Irrigation Applications
Bibliographic record
Abstract
We compared the impact of surface broadcasted N-Viro biosolids and inorganic fertilizer (16.5% Ammonium sulphate, 34.5% Diammonium phosphate, 4.5% Potash, and 44.5% s and/or clay filler) applications on soil properties and nutrients, leaf nutrient concentration, and the fruit yield of lowbush blueberry under irrigated and nonirrigated conditions during 2008-2009 at Debert, NS, Canada. Application rates of N-Viro biosolids were more than double of inorganic fertilizer applied at a recommended N rate of 32 kg ha−1. The experimental treatments NI: N-Viro with irrigation, FI: inorganic fertilizer with irrigation, N: N-Viro without irrigation, and F: inorganic fertilizer without irrigation (control) were replicated four times under a randomized complete block design. The NI treatment had the highest OM (6.68%) followed by FI (6.32%), N (6.18%), and F (4.43%) treatments during the year 2008. Similar trends were observed during 2009 with the highest soil OM values (5.50%) for NI treatment. Supplemental irrigation resulted in a 21% increase in the ripe fruit yield. Nonsignificant effect of fertilizer treatments on most of the nutrient concentrations in soil and plant leaves, and on ripe fruits yield reflects that the performance of N-Viro was comparable with that of the inorganic fertilizer used in this study.
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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) | 0.000 | 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 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".