Boundary-Line Approach to Determine Minimum and Maximum Leaf Micronutrient Concentrations in Wild Lowbush Blueberry in Quebec, Canada
Bibliographic record
Abstract
Minimum and maximum leaf micronutrient concentrations in wild lowbush blueberry (Vaccinium angustifolium Ait.) were determined under the climatic and edaphic conditions of the Saguenay–Lac-Saint-Jean region (Quebec, Canada). The boundary-line approach was used to determine the relationship between leaf micronutrient concentrations and yield. The data were obtained from nitrogen and phosphorus fertilization trials conducted from 2001 to 2008 on 13 commercial lowbush blueberry fields in the Saguenay-Lac-Saint-Jean region. On average, more than 80% of the samples met the new minimum leaf micronutrient concentrations. Minimum leaf concentrations were revised downward for aluminum (Al), copper (Cu), iron (Fe), and zinc (Zn) compared to actual reference values. Minimum leaf boron (B) and manganese (Mn) concentrations were revised upward. Maximum leaf concentrations for all micronutrients were also revised downward. Minimum and maximum leaf concentrations were 26.2–73.5, 32.2–52.9, 3.2–6.5, 27.8–61.4, 873–1394, and 11.0–17.3 mg kg−1 for Al, B, Cu, Fe, Mn, and Zn, respectively. The determination of these new minimum and maximum leaf micronutrient concentrations established sufficiency ranges for the growing conditions in the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".