Interannual Variation and Trend of Carbon Budget Observed for More Than Two Decades at Takayama in a Cool‐Temperate Deciduous Forest in Central Japan
Bibliographic record
Abstract
Abstract Long‐term carbon dioxide (CO2) flux measurements between the atmosphere and the ecosystem have been made since 1993 at a cool‐temperate deciduous forest site (Takayama) in Japan influenced by the Asian Monsoon, constituting the longest data set among all the AsiaFlux sites. Interannual variations (IAVs) and trends in the annual carbon budget components and related environmental factors were examined. Annual net ecosystem production (NEP) (mean ± 1σ) during the period of eddy covariance measurements in 1999–2021 was 265 ± 86 gC m−2 yr−1, and its IAV was dependent more on gross primary production (GPP) than on ecosystem respiration. The annual NEP and GPP were correlated with the monthly mean NEP, GPP and leaf area index from June to September, as well as with the length of the net carbon uptake period. Significantly increasing and decreasing trends in the annual NEP and GPP were detected during 2004–2013 and 2013–2021, respectively. The increasing trends were mainly caused by the vegetation recovery from typhoon disturbances. On the other hand, although the cause of the decreasing trends was not clearly identified, recent extreme weather events and/or forest succession might be related to the trends. Significant positive correlations between the start and the end of the net carbon uptake period, and between the leaf expansion and leaf fall were found. These may be attributed to phenological characteristics and negative correlation between air temperature in spring and solar radiation in early fall associated with ENSO events that can also influence IAVs in annual NEP and GPP.
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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 | 0.001 | 0.001 |
| 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 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".