Nutrient sufficiency levels for haskap (<i>Lonicera caerulea</i> L.) using the boundary-line approach
Bibliographic record
Abstract
Adequate supply of plant nutrients is crucial for haskap plant growth and increased productivity. A study was carried out to determine the variability in haskap (Lonicera caerulea L. cv. Indigo Gem) plant characteristics in relation to soil and leaf tissue nutrient status. A total of 19 composite soil samples and corresponding plant leaf tissue samples were collected in 2016 from 12 locations in Nova Scotia. Plant parameters measured include growth rate, leaf size, leaf chlorophyll content, and visual observations. A boundary-line approach was used to determine nutrient sufficiency ranges in leaf tissues of 2.23%–2.96% for N, 0.22%–0.28% for P, 0.84%–1.32% for K, 1.63%–2.10% for Ca, and 0.14%–0.50% for Mg. Principal component and correlation analysis suggested a possible antagonistic interaction between leaf K and Mg. Negative associations were observed most frequently between Ca and Mg and other nutrients, especially K. Plant parameters such as bush volume, leaf size, and growth rate were closely related to soil and leaf K. Deficiencies in leaf tissue K and P were identified as potentially important factors limiting growth. Therefore, there is a need to adjust or balance the application of these nutrients. In conclusion, the sufficiency ranges derived can be used as a guiding principle in diagnosing the nutritional status of haskap cv. Indigo Gem on representative farms in Nova Scotia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".