The application and interpretation of Keeling plots in terrestrial carbon cycle research
Bibliographic record
Abstract
Photosynthesis and respiration impart distinct isotopic signatures to the atmosphere that are used to constrain global carbon source/sink estimates and partition ecosystem fluxes. Increasingly, the “Keeling plot” method is being used to determine the carbon isotope composition of ecosystem respiration (δ 13 C R ) in order to better understand the processes controlling ecosystem isotope discrimination. In this paper we synthesize emergent patterns in δ 13 C R by analyzing 146 Keeling plots constructed at 33 sites across North and South America. In order to interpret results from disparate studies, we discuss the assumptions underlying the Keeling plot method and recommend standardized methods for determining δ 13 C R . These include the use of regression calculations that account for error in the x variable, and constraining estimates of δ 13 C R to nighttime periods. We then recalculate δ 13 C R uniformly for all sites. We found a high degree of temporal and spatial variability in C 3 ecosystems, with individual observations ranging from −19.0 to −32.6‰. Mean C 3 ecosystem discrimination was 18.3‰. Precipitation was a major driver of both temporal and spatial variability of δ 13 C R , suggesting (1) a large influence of recently fixed carbon on ecosystem respiration and (2) a significant effect of previous climatic effects on δ 13 C R . These results illustrate the importance of water availability as a key control on atmospheric 13 CO 2 and highlight the potential of δ 13 C R as a useful tool for integrating environmental effects on dynamic canopy and ecosystem processes.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".