Foliar nutrient concentrations and potential limitations of white spruce (Picea glauca (Moench) Voss) in Yukon, Canada
Bibliographic record
Abstract
"Although nutrient deficiencies are not uncommon in forests across the north, little is known about these limitations in the Yukon, and even less about how these limitations have been and/or will be affected by climate. To address existing edaphic limitations to forests in the Yukon, an investigation of the nutrient concentrations of white spruce (Picea glauca (Moench) Voss) foliage was undertaken throughout various regions of Yukon in 2009. By comparing individual nutrient concentrations to critical values and reviewing nutrient ratios, the results identified nitrogen (N) as being commonly severely deficient. Phosphorus (P) and sulphur (S) were also commonly deficient, whereas magnesium (Mg) and potassium (K) were mostly adequate with few reports of slight deficiency levels. In contrast, calcium (Ca) was adequate at all locations. Of the micronutrients, zinc (Zn) and manganese (Mn) were the only elements in adequate supply at all sites while slight to moderate deficiencies were commonly indicated for all other micronutrients across the study area. Nutrient limitations may ultimately restrict the growth response of white spruce to climate changes and/or increasing atmospheric CO₂."
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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 | 0.002 |
| 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) | 0.001 | 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".