Using stable carbon isotopes of lignin-derived methoxy to improve historical apportionments of particulate organic matter and sediment sources incorporating multiple Suess corrections
Bibliographic record
Abstract
Abstract Purpose Soil erosion models are essential to improving sediment management strategies. Sediment source fingerprinting is used to help validate erosion models. Fingerprinting sediment sources with organic isotopic tracers faces challenges from aquatic sources and co-linearity. To address these complexities, integrating another land-use-specific tracer is essential. Suess corrections incorporating multiple mean-residence-times are necessary to accurately model historical sediment apportionments. In previous studies, compound specific isotopic tracers indicated forest as the dominant source. We hypothesize that there is an overestimation of forest contribution, attributed to the misclassification of particulate organic matter as forest. Methods In this study, we utilize stable carbon isotope (δ 13 C) values of fatty acids and the average chain length in combination with the δ 13 C values of lignin-derived methoxy groups as an additional tracer. We apply different Suess corrections to explore the effect of the changing atmospheric δ 13 CO 2 values on sediment apportionment. The performance of the unmixing model is evaluated with 300 mathematical mixtures. To determine shifts in sediment sources throughout the last 130 years, particulate organic matter contributions are determined and removed to apportion sediment soil sources. We investigate the potential misclassification of forest contributions by merging particulate organic matter and forest sources to simulate tracers which are unable to discriminate. Results The inclusion of δ 13 C values of lignin methoxy groups and the alkane average chain length as additional tracers successfully removed tracer co-linearity. Additionally, we used an updated concentration dependent point in polygon test to identify sediment with increased potential for incorrect source apportionments. Changes in the dominant sediment sources over time (Forest: pre-1990, Pasture: 1910–1940, Arable: post 1940) highlight the effect of policy-induced land-use changes. Additionally, the inability to discriminate particulate organic matter and forest sources was revealed to cause a 37% overestimation of forest contributions from 1944 to 1990. Conclusion Using δ 13 C values of lignin methoxy groups as an additional tracer, we identified critical points in the 130-year sediment history of Lake Baldegg. Furthermore, we highlight the importance of incorporating multiple Suess effects. Through mathematical mixtures, we assessed the confidence that should accompany apportionment estimates. While merging forest and particulate organic matter sources did not result in forest as the dominant source over the last 130 years, separating these sources resulted in more accurate apportionment. These insights offer valuable information to enhance the accuracy of sediment fingerprinting, which can then be used to assist soil erosion models employed for sediment mitigation policies.
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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 | 0.000 |
| 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 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".