Tree Ring Reconstructions of Stemwood Biomass Indicate Increases in the Growth Rate of Black Spruce Trees Across Boreal Forests of Canada
Bibliographic record
Abstract
Abstract The claim that changes in atmospheric composition and climate have enhanced the growth rate of trees is prevalent in science, yet it is not supported by many recent tree ring studies. In this study, we analyzed historical time trends in stemwood biomass growth derived from black spruce (Picea mariana(Mill.) BSP) trees at 248 plots across Canada. The sample consisted of trees that were live and dominant at the time of sampling (LDS). Observations of stemwood biomass of LDS trees at a reference age of 75 years (Bsw75,LDS) increased by 154 to 321% over 1901–2001 depending on the method of trend estimation. Simulations from a calibrated individual‐based Growth and Yield model—forced with varying degrees of hypothetical trend in tree growth—were used to estimate the proportion of trend that could be attributed to intrinsic factors, including artefacts introduced by sampling from LDS trees instead of from the population of trees. Imposing no time trend in simulated tree growth, stemwood biomass of 75‐year‐old LDS trees (Bsw75,LDS,Model) increased by 63% (41 to 85% CI). We conclude that the remaining variation in growth of LDS trees (154 to 321% minus 63% = 91 to 258%) can be attributed to net extrinsic forcing. The scaling relationship between LDS and population trees further suggested that stemwood biomass growth of the population (Gsw75,POP) increased by 47 to 82%. By accounting for both natural dynamics and artefacts of the sampling design in estimation of net intrinsic forcing, we gained confidence that growth rate of black spruce trees across Canada increased significantly over 1901–2001. While growth enhancement is consistent with beneficial effects of increasing levels of reactive nitrogen, carbon dioxide, and warming, there remains uncertainty in the degree that applied procedures fully account for known sampling artefacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".