Stable Isotopes and Carbon Cycle Processes in Forests and Grasslands
Bibliographic record
Abstract
Abstract:Scaling and partitioning are frequently two difficult challenges facing ecology today. With regard to ecosystem carbon balance studies, ecologists and atmospheric scientists are often interested in asking how fluxes of carbon dioxide scale across the landscape, region and continent. Yet at the same time, physiological ecologists and ecosystem ecologists are interested in dissecting the net ecosystem CO2exchange between the biosphere and the atmosphere to achieve a better understanding of the balance between photosynthesis and respiration within a forest. In both of these multiple‐scale ecological questions, stable isotope analyses of carbon dioxide can play a central role in influencing our understanding of the extent to which terrestrial ecosystems are carbon sinks. In this synthesis, we review the theory and present field evidence to address isotopic scaling of CO2fluxes. We first show that the13C isotopic signal which ecosystems impart to the atmosphere does not remain constant over time at either temporal or spatial scales. The relative balances of different biological activities and plant responses to stress result in dynamic changes in the13C isotopic exchange between the biosphere and atmosphere, with both seasonal and stand‐age factors playing major roles influencing the13C biosphere‐atmosphere exchange. We then examine how stable isotopes are used to partition net ecosystem exchange fluxes in order to calculate shifts in the balance of photosynthesis and respiration. Lastly, we explore how fundamental differences in the18O isotopic gas exchange of forest and grassland ecosystems can be used to further partition terrestrial fluxes.
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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".