Seasonal controls on the exchange of carbon and water in an Amazonian rain forest
Bibliographic record
Abstract
The long‐term resilience of Amazonian forests to climate changes and the fate of their large stores of organic carbon depend on the ecosystem response to climate and weather. This study presents 4 years of eddy covariance data for CO2 and water fluxes in an evergreen, old‐growth tropical rain forest examining the forest's response to seasonal variations and to short‐term weather anomalies. Photosynthetic efficiency declined late in the wet season, before appreciable leaf litter fall, and increased after new leaf production midway through the dry season. Rates of evapotranspiration were inelastic and did not depend on dry season precipitation. However, ecosystem respiration was inhibited by moisture limitations on heterotrophic respiration during the dry season. The annual carbon balance for this ecosystem was very close to neutral, with mean net loss of 890 ± 220 kg C ha−1 yr−1, and a range of −221 ± 453 (C uptake) to +2677 ± 488 (C loss) kg C ha−1 yr−1 over 4 years. The trend from large net carbon release in 2002 towards net carbon uptake in 2005 implies recovery from prior disturbance. The annual carbon balance was sensitive to weather anomalies, particularly the timing of the dry‐to‐wet season transition, reflecting modulation of light inputs and respiration processes. Canopy carbon uptake rates were largely controlled by phenology and light with virtually no indication of seasonal water limitation during the 5‐month dry season, indicating ample supplies of plant‐available‐water and ecosystem adaptation for maximum light utilization.
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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 | 0.000 | 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) | 0.000 | 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".