That's phat: The development and evaluation of compound-specific stable isotopes (CSSIs) of very long-chain fatty acids (VLCFAs) as a sediment tracing tool in a temperate climate
Bibliographic record
Abstract
Rivers and lakes are subject to various forms of pollution from anthropogenic activity and natural sources. Many pollutants, such as those from agricultural runoff, are transported in rivers by binding to sediment. Sediment itself is a pollutant, and is often an indicator of terrestrial erosive processes. Common methods of establishing sediment provenance on a broad scale include the use of fallout radio nuclides and geochemistry. In 2008, Dr. Max Gibbs published a sediment tracing article on the use of compound-specific stable isotopes (CSSIs) of plant origin which identified sediment sources based on land use in sub-tropical New Zealand. The objective of the research undertaken here was to apply the CSSI concept to agricultural watersheds in a northern, temperate climate. Two watersheds were selected: the Horsefly River Watershed (HRW) near Horsefly, BC, Canada and the South Tobacco Creek Watershed (STCW), near Miami, Manitoba, Canada. The HRW represented a mostly pristine watershed. The STCW represented a heavily cropped agricultural watershed. The HRW samples were used to develop laboratory methodology, while the STCW samples were used to evaluate the CSSI technique using carbon stable isotopes. The dissertation addresses the following: (i) literature review of plant biomarkers and spatial/temporal variability of CSSIs due to biological, environmental and analytical factors; (ii) methodology, analysis and variability associated with bulk soil and sediment isotope determination; (iii) methodologies for processing soil and sediment from sampling to isotope analysis; and (iv) spatial and temporal variability of CSSI tracers. The CSSI tracers were evaluated to reveal spatial and temporal variability of VLCFA concentrations and isotope signatures at the point, transect and field scales. Weighted t-tests were used to differentiate sediment sources spatially and temporally. The use of bulk carbon as a proxy for VLCFA concentrations in source apportioning was also explored. The work presented here demonstrates the ability of CSSIs to differentiate sediment sources based on land use. The development of analytical methods and the resulting analysis of soil and sediment extracts have indicated that VLCFAs may be isolated and quantified to generate reliable isotope data. The methods will hopefully lead to the standardization of CSSIs protocols.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".