Etiology of a recent white spruce decline: role of potassium deficiency, past disturbances, and climate change
Bibliographic record
Abstract
Along with climate, multiple global or large-scale change agents shape forest ecosystem health. We present a case study where we attempted to elucidate the driving factors causing decline symptoms in white spruce (Picea glauca (Moench) Voss) in young spruce–fir boreal stands. Tree defoliation rate in the studied areas was related to the foliage discoloration intensity of the 2-year old needles and the decreasing stem basal area increment from 1997 to 2008. The onset of this growth decline in 1997 coincided with the occurrence of extremes for four climatic indices. The foliage of affected trees was deficient in K. The relationship between tree decline and K deficiency was tested through a diagnostic fertilization trial using a two-level factorial combination of N, K, and Mg. The trial indicated that K was the single limiting nutrient among the three tested elements. A single K addition increased stem basal area by 43% on average after 11 years. It is hypothesized that the poor K status of trees can be attributed to a series of natural and anthropogenic disturbances along with past forest management activities. Climate change in the region since the last decades also may have contributed to exacerbate K deficiency in such forest ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".