Integration of sediment fingerprinting techniques and sampling approaches within a prairie watershed in southern Alberta
Bibliographic record
Abstract
Soil erosion can cause sedimentation and eutrophication of waterbodies, which can decrease the quality of water. This study took place in southern Alberta within the Lower Little Bow River (LLBR) watershed, which has been a source of water quality studies as a part of the Oldman River Basin Water Quality Initiative. The objectives of this study were, firstly, to identify and apportion sources of suspended sediment using a sediment fingerprinting technique and the mixing model, MixSIAR, within a small 6-km reach of the LLBR, secondly, to build a better understanding of colour properties as tracers, and thirdly, to explore the effect of reach sampling within using the MixSIAR model. The first objective was accomplished through the sampling of multiple watershed sources including Agricultural Land, Coulee Walls, Stream Banks, an Irrigation Return-Flow channel and Upstream sediment. Suspended sediment was collected within the reach itself and un-mixed to determine source proportions contributed by each of the five potential sediment sources. The second objective was accomplished by taking three approaches in order to determine the appropriate environmental tracer combination to apportion sources accurately and how these affect choosing appropriate environmental tracers for sediment fingerprinting. The third objective of this thesis was accomplished by determining the composition and incorporation of upstream inflowing sediment into a watershed reach when conducting a sediment fingerprinting study. The mixing model, MixSIAR, was used as a tool to manipulate the watershed in order to determine how to improve the efficiency of the sediment fingerprinting process regarding tracer selection and sampling approaches. The MixSIAR model is used as a tool to determine how to design an approach to a reach within a watershed and the significance of upstream inflowing sediment inclusion as a sediment source.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".