It's sedimentary, my dear Watson : implications of watershed processes on the sediment fingerprinting approach
Bibliographic record
Abstract
The sediment source fingerprinting approach is based on the assumption that the potential sources of sediment within a watershed can be linked to in-stream sediment by using the inherent physical or biogeochemical characteristics of the sediment (i.e., sediment properties) as fingerprints.At present, one of the main limitations of the sediment source fingerprinting approach is the ability to link sediment back to their sources due to the nonconservative nature of many sediment properties.Ideally, sediment properties do not change as the sediment (i.e., transported unconsolidated soil or rock particles) move through a watershed allowing for a direct comparison between sources and sediment.However, sediment collected downslope or downstream from its source is often found to have a finer grain-size distribution and a higher organic matter content as compared to the source material as the smaller and less dense particles are preferentially mobilized and transported.Accounting for changes in both particle size distribution and organic matter content are important as many fingerprint concentrations are correlated with both properties, but it is unclear as to what is the best approach to account for these changes.In an effort to provide a more reliable and robust link between sources and sediment, a series of experimental and observational studies were conducted to investigate the factors that control particle size and organic matter selectivity and their subsequent effect on a broad suite of geochemical fingerprints.These studies investigated particle selectivity at the landscape scale, represented by a sequence of hillslope, riparian and fluvial environments.Processes within each of these three environments were found to preferentially mobilize and transport fine-grained and organic-rich particles.Many commonly used particle size and organic matter correction
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".