Lost and found: Maximizing the information from a series of bedload tracer surveys
Bibliographic record
Abstract
Abstract Bedload particle tracking is a technique used to better understand sediment dynamics in rivers. Despite technical advances, tracers may be missed in field surveys. The missed tracers may bias the study results even where recovery rates are high, for example if they are preferentially buried close to the seeding site or transported downstream of the surveyed reach. The goal of the current study is to demonstrate that more information can be extracted from a series of bedload tracer surveys by carefully considering the fate of missing and found tracers and implementing a set of strategies to include the (incomplete) information on sediment displacement metrics. A set of open‐source Matlab algorithms collectively called PITtrack are described that perform the calculations. Results from two tracer datasets show that commonly used sediment displacement metrics are sensitive to the inclusion of the missing tracers, even for cases with high recovery rates. Metrics that describe the variance and skewness of the tracers as they disperse are particularly sensitive. The recommended strategy is to include (a) inferred positions of tracers that are missing but unmoved, (b) likely positions of tracers that are missing, moved, and movement can be attributed to a survey period within the uncertain period that meets a dominant flood criterion, and (c) last known positions of tracers considered lost because they go missing and are never found again. Overall the results offer a method to include all available information on missing tracers to better understand sediment dispersion. Future work should be done to assess the classification system for a wider range of field sites and further refine classification based on spatial or other information.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".