Sensitivity of source sediment fingerprinting to tracer selection methods
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last 2 decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, modelled contributions and performance on a single dataset. As for the three-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracer properties analysed in soil (three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was then used to predict source contributions on both virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their sensitivity to the conservative behaviour of properties, which was found to be associated with different predicted source contribution tendencies along the sediment core. The methods selecting the largest number of tracers were associated with a dominant and constant contribution of forests to sediment. In contrast, the methods selecting the lowest number of tracers were associated with a dominant and constant contribution of cropland to sediment. Furthermore, the intermediate selection of tracers led to more balanced contributions of both cropland and forest to sediments. The prediction of the virtual mixtures allowed us to compute several evaluation metrics, which are generally used to support the evaluation of model accuracy for each tracer selection method. However, strong differences or the absence of correspondence were observed between the range of predicted contributions obtained for virtual mixtures and those values obtained for actual sediments. These divergences highlight the fact that evaluation metrics obtained for virtual mixtures may not be directly transferable to models run for actual samples and must be interpreted with caution to avoid over-interpretation or misinterpretation. These divergences may likely be attributed to the occurrence of a not (fully) conservative behaviour of potential tracer properties during erosion, transport and deposition processes, which could not be fully reproduced when generating the virtual mixtures with currently available methods. Future research should develop novel metrics to quantify the conservative behaviour of tracer properties during erosion and transport processes. Furthermore, new methods should be designed to generate virtual mixtures closer to reality and to better evaluate model accuracy. These improvements would contribute to the development of more reliable sediment fingerprinting techniques, which are needed to better support the implementation of effective soil and water conservation measures at the catchment scale.
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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 | 0.001 | 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 it