Testing for a CO<sub>2</sub>fertilization effect on growth of Canadian boreal forests
Bibliographic record
Abstract
[1] The CO2 fertilization hypothesis stipulates that rising atmospheric CO2 has a direct positive effect on net primary productivity (NPP), with experimental evidence suggesting a 23% growth enhancement with a doubling of CO2. Here, we test this hypothesis by comparing a bioclimatic model simulation of NPP over the twentieth century against tree growth increment (TGI) data of 192 Pinus banksiana trees from the Duck Mountain Provincial Forest in Manitoba, Canada. We postulate that, if a CO2 fertilization effect has occurred, climatically driven simulations of NPP and TGI will diverge with increasing CO2. We use a two-level scaling approach to simulate NPP. A leaf-level model is first used to simulate high-frequency responses to climate variability. A canopy-level model of NPP is then adjusted to the aggregated leaf-level results and used to simulate yearly plot-level NPP. Neither model accounts for CO2 fertilization. The climatically driven simulations of NPP for 1912–2000 are effective for tracking the measured year-to-year variations in TGI, with 47.2% of the variance in TGI reproduced by the simulation. In addition, the simulation reproduces without divergence the positive linear trend detected in TGI over the same period. Our results therefore do not support the attribution of a portion of the historical linear trend in TGI to CO2 fertilization at the level suggested by current experimental evidence. A sensitivity analysis done by adding an expected CO2 fertilization effect to simulations suggests that the detection limit of the study is for a 14% growth increment with a doubling of atmospheric CO2 concentration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".