Stream bank erosion as a source of sediment within New Zealand catchments
Bibliographic record
Abstract
ABSTRACT Stream bank erosion has been anecdotally identified as an important source of sediment in New Zealand catchments, however, there have been few attempts to quantify its contribution. Here we use a radionuclide‐based sediment tracing approach to determine the relative contribution of stream bank‐ and hillslope‐derived sediment within three catchments in the upper North Island of New Zealand. Both lithogenic (radium‐226 and radium‐228) and fallout radionuclides (caesium‐137 and excess lead‐210) were used to differentiate sediment derived from stream bank and hillslope sources. The relative contribution of stream banks and hillslopes to fluvially transported suspended sediment were predicted using a mixing model approach. Our results indicate that both fallout and lithogenic radionuclides provide good source differentiation. We demonstrate that stream bank erosion can contribute very high proportions of sediment within New Zealand catchments. We used independent assessments of bank erosion from each of the study catchments to support the sediment source fingerprinting results. Further work is required to determine the spatial and temporal variability of the contribution of sediment from stream banks. Information on the importance of different sediment sources is needed to target limited catchment rehabilitation resources where they will have the most impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".