Forest composition influences how seasonal climate variables affect white spruce (<i>Picea glauca</i> (Moench) Voss) growth
Bibliographic record
Abstract
Variation in annual white spruce growth (Picea glauca (Moench) Voss) has been shown to be dependent on weather conditions such as air temperature and moisture availability. However, questions remain about how intra-annual variation in climate variables influence annual growth, and whether stand composition and structure can influence climate conditions and the trees’ responses to weather stress. We evaluated the importance and influence of seasonal climate on growth (annual ring width increment) of 32-year-old white spruce trees in pure and mixedwood stands in northeastern British Columbia. The importance of climate variables, and their ranked order, differed between pure and mixedwood stands. Soil water potential (SWP) during spring and summer were the main factors influencing spruce growing in both pure and mixedwood stands. However, the relative importance of each variable, their direct effects, and their interactions differed between stand types. Warm springs increased spruce growth in both stands, while warm summers increased spruce growth in the pure spruce stand but decreased growth in the mixedwood stand. Spruce growth in the pure stand was positively correlated with soil water potential during spring and summer, while spruce growth in the mixedwood stand was negatively correlated. In both stand types, there was an interplay between the amount of water available in the soil and air temperature to influence annual growth. Our findings suggest stand composition influences the resilience of spruce to drought.
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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) | 0.001 | 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".