Seasonal controls on interannual variability in carbon dioxide exchange of a near‐end‐of rotation Douglas‐fir stand in the Pacific Northwest, 1997–2006
Bibliographic record
Abstract
Abstract This study analyzes 9 years of eddy‐covariance (EC) data carried out in a Pacific Northwest Douglas‐fir (Pseudotsuga menzesii) forest (58‐year old in 2007) on the east coast of Vancouver Island, Canada, and characterizes the seasonal and interannual variability in net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (Re) and primary climatic controls on these fluxes. The annual values (± SD) of NEP, GPP and Re were 357 ± 51, 2124 ± 125, and 1767 ± 146 g C m−2 yr−1, respectively, with ranges of 267–410, 1592–2338, and 1642–2071 g C m−2 yr−1, respectively. Spring to early summer (March–June) accounted for more than 80% of annual NEP while late spring to early autumn (May–August) was mainly responsible for its interannual variability (∼80%). The major drivers of interannual variability in annual carbon (C) fluxes were annual and spring mean air temperatures (Ta) and water deficiency during late summer and autumn (July–October) when this Douglas‐fir forest growth was often water‐limited. Photosynthetically active radiation (Q), and the combination of Q and soil water content (θ) explained 85% and 91% of the variance of monthly GPP, respectively; and 91% and 96% of the variance of monthly Re was explained by Ta and the combination of Ta and θ, respectively. Annual net C sequestration was high during optimally warm and normal precipitation years, but low in unusually warm or severely dry years. Excluding 1998 and 1999, the 2 years strongly affected by an El Niño/La Niña cycle, annual NEP significantly decreased with increasing annual mean Ta. Annual NEP will likely decrease whereas both annual GPP and Re will likely increase if the future climate at the site follows a trend similar to that of the past 40 years.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".