Technical note: Comparison of radiometric techniques for estimating recent organic carbon sequestration rates in inland wetland soils
Bibliographic record
Abstract
For wetlands to serve as natural climate solutions, accurate estimates of organic carbon (OC) sequestration rates in wetland sediments are needed. Dating using cesium-137 ( 137 Cs) and lead-210 ( 210 Pb) radioisotopes is commonly used for measuring OC sequestration rates in wetland sediments. 137 Cs radioisotope dating is relatively simple, with calculations based on a single point representing the onset (1954) or peak (1963) of the 137 Cs fallout. 210 Pb radioisotope dating is more complex, as the calculations are based on multiple points. Here, we show that reliable dating of sediment cores collected from wetlands can be achieved using either 137 Cs or 210 Pb dating or their combination. However, 137 Cs and 210 Pb profiles along the depth of sediment cores need to be screened, analyzed, and interpreted carefully to estimate OC sequestration rates with high precision. To this end, we propose a decision framework for screening 137 Cs and 210 Pb profiles into high- and low-quality sediment profiles, and we compare dating using the 1954 and 1963 time markers, i.e., the rates of sedimentation and, consequently, OC sequestration over the past ∼ 60 years. Our findings suggest that 137 Cs- and 210 Pb-based OC sequestration rates are comparable, especially when using the 1963 (vs. 1954) time marker.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".