Selecting Color-based Tracers and Classifying Sediment Sources in the Assessment of Sediment Dynamics Using Sediment Source Fingerprinting
Bibliographic record
Abstract
The use of sediment color as a fingerprint property to determine sediment sources is an emerging technique that can provide a rapid and inexpensive means of investigating sediment sources. The present study aims to test the feasibility of color fingerprint properties to apportion sediment sources within the South Tobacco Creek Watershed (74 km2) in Manitoba, Canada. Suspended sediment from 2009 to 2011 at six monitoring stations and potential source samples along the main stem of the creek were collected. Reflectance spectra of sediments and source materials were quantified using a diffuse reflectance spectrometry, and 16 color coefficients were derived from several color space models. Canonical discriminant analysis was used to reclassify and downsize sediment source groups. After the linear additive test and stepwise discriminant function analysis, four color coefficients were chosen to fit the Stable Isotope Analysis in R model. Consistent with the conventional fingerprinting approach, the color fingerprint results demonstrated a switch in the dominant sediment source between the headwaters and the outlet of the watershed, with the main sources being topsoil in the upper reaches, whereas outcrop shale and stream bank materials dominated in the lower reaches. The color fingerprinting approach can be integrated with conventional fingerprints (e.g., geochemical and fallout radionuclide properties) to improve source discrimination, which is a key component for source ascription modeling. We concluded that the use of color fingerprints is a promising, cost-effective technique for sediment source fingerprinting. Core Ideas Defining sources and testing the behavior of tracers are critical for sediment source apportionment. The predominant sources of river sediment varied at different reaches of the study creek. Integrating color and conventional fingerprinting techniques likely improves source apportionment. Color fingerprinting is a promising, cost-effective technique for sediment source ascription.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".