Structural Carbon Allocation and Wood Growth Reflect Climate Variation in Stands of Hybrid White Spruce in Central Interior British Columbia, Canada
Bibliographic record
Abstract
Research Highlights: This research presents a novel approach for comparing structural carbon allocation to tree growth and to climate in a dendrochronological analysis. Increasing temperatures reduced the carbon proportion of wood in some cases. Background and Objectives: Our goal was to estimate the structural carbon content of wood within hybrid white spruce (Picea glauca (Moench) × engelmannii (Parry) grown in British Columbia, Canada, and compare the percent carbon content to wood properties and climate conditions of the region. Specific objectives included: (i) the determination of average incremental percent carbon, ring widths (RW), earlywood (EW) and latewood (LW) widths, cell wall thickness, and density over time; (ii) the determination of differences between percent carbon in individual forest stands and between regions; and (iii) the evaluation of the relationships between percent carbon and climate variation over time. Methods: Trees were sampled from twelve sites in northern British Columbia. Wood cores were analyzed with standard dendrochronology techniques and SilviScan analysis. Percent structural carbon was determined using acetone extraction and elemental analysis for 5 year increments. Individual chronologies of wood properties and percent carbon, and chronologies grouped by region were compared by difference of means. Temperature and precipitation values from the regions were compared to the carbon chronologies using correlation, regression, and visual interpretation. Results: Significant differences were found between the percent structural carbon of wood in individual natural and planted stands; none in regional aggregates. Some significant relationships were found between percent carbon, RW, EW, LW, and the cell wall thickness and density values. Percent carbon accumulation in planted stands and natural stands was found in some cases to correlate with increasing temperatures. Natural stand percent carbon values truncated to the last 30 years of growth was shown as more sensitive to climate variation compared to the entire time series. Conclusions: Differences between the stands in terms of structural carbon proportion vary by site-specific climate characteristics in areas of central interior British Columbia. Wood properties can be good indicators of variation in sequestered carbon in some stands. Carbon accumulation was reduced with increasing temperatures; however, warmer late-season conditions appear to enhance growth and carbon accumulation.
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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 | 0.000 | 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) | 0.000 | 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".