Keeling plots are non‐linear in non‐steady state diffusive environments
Bibliographic record
Abstract
End‐member mixing models, such as the Keeling and Miller‐Tans plots, are frequently used to interpret isotopic data collected from environments where mass transfer occurs due to diffusive processes, however, researchers do not commonly consider the effect of diffusive kinetic fractionation on assumptions of linearity in these mixing models. Risk and Kellman (2008) recently showed the potential for non‐linearity in the Keeling plot approach, but their simplified model offers only a first order approximation of this effect in complex systems such as soils. Here we use 3‐D numerical simulations and measurements of soil δ 13 C‐CO 2 flux accumulating in a static head space chamber to conclusively show that in diffusive environments Keeling plots are non‐linear, violating key assumptions of the technique and potentially creating a large source of error in data analysis and interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".