Annual basal area increment and growth duration of <i>Pinus taeda</i> in response to eight years of free‐air carbon dioxide enrichment
Bibliographic record
Abstract
Abstract Rising CO2 is predicted to increase forest productivity, although the duration of the response and how it might be altered by variation in rainfall, temperature and other environmental variables are not well understood. We measured the basal area of rapidly growing Pinus taeda trees exposed to free‐air CO2 enrichment for 8 years and used these measurements to estimate monthly and annual growth. We used these measurements in a statistical model to estimate the start and end of growth in each year. Elevated CO2 increased the basal area increment (BAI) of trees by 13–27%. In most years, exposure to elevated CO2 increased the growth rate but not the duration of the active growth period. With the exception of 1 year following an extreme drought and a severe ice storm, BAI was positively correlated with the amount of rainfall during the active growth period. The interannual variation in the relative enhancement of BAI caused by elevated CO2 was strongly related to temperature and rainfall, and was greatest in years with high vapor pressure deficit. There was no evidence of a systematic reduction in the stimulation of growth during the first 8 years of this experiment, suggesting that the hypothesized limitation of the CO2 response caused by nitrogen availability has yet to occur.
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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".