Evaluating and improving the assessment of compound-specific stable isotope derived sediment fingerprinting results in an agricultural watershed in British Columbia, Canada
Bibliographic record
Abstract
• Compound specific stable isotopes successfully discriminated across land use types. • Multiple statistical analyses showed significant overlap among agricultural sources. • The primary source of sediment in Murray Creek is agriculture, followed by banks. • Reducing field and bank erosion is paramount for aquatic ecosystem health. Agricultural fields are a known contributor of sediment to streams and rivers, but determining specific sources of sediment in agricultural watersheds characterized primarily by C3 plants has proven difficult with traditional sediment fingerprinting methods. This study aimed to use compound-specific stable isotopes of long-chain fatty acids (LCFAs) to determine the sediment contribution from multiple sources – cropped, grazed, forage, riparian zones, banks, and forested soils – to Murray Creek, a tributary to the Nechako River in British Columbia, Canada. Source and sediment samples were collected in 2019 and analysed for LCFA concentrations and δ 13 C FA values (C20:0-C30:0, C32:0). Statistical analyses were undertaken to determine the discrimination capabilities of the LCFAs. Results showed that discrimination was poor across the agricultural land uses, though forested samples were clearly identified. For mixing in Murray Creek, just three sources – agriculture (including riparian areas), forested, and banks – were used. The results found agriculture and banks to be the primary sources of sediment. This is important because Murray Creek delivers sediment to important fish spawning habitat, which has been identified as one of multiple causes of fish population declines. The difficulty in discriminating between the agricultural land use types reflects multiple confounding factors including the multi-use nature of agricultural land in Murray Creek (i.e., land can be used as harvested forage and unmanaged grazing in the same year), the similarities in isotopic signatures across C3 plants, and the temporal insensitivity of the analysis, which may pick up the vegetation signatures of previous years. While the LCFAs were not able to identify specific fields of importance in the timeframe of this study, this technique would be valuable if the sources were more unique, if more samples of each source were taken for better characterization, and if previous land use in the agricultural fields was incorporated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".