Pool‐riffle sedimentation and surface texture trends in a gravel bed stream
Bibliographic record
Abstract
Abstract A 3 year field campaign was completed to investigate spatial and temporal variability of sedimentation trends for a single pool‐riffle pair located in the Santa Cruz Mountains, California. Our measurements represent a range of hydrologic conditions over 11 sediment‐mobilizing events. Two different statistical methods were used to explore riffle sedimentation. Cochran's Q and McNemar's nonparametric tests (one method) indicate that riffle sediment surface texture was spatially and temporally varied at the transect level. For McNemar's test, variation was significant at , with several trends evident, including strong riffle fining triggered by a 20 year flood event. A nonlinear empirical orthogonal function method known as self‐organizing maps (SOMs; the second method) shows that riffle sediment surface texture is well described by two characteristic temporal signals, and one transitional signal at the sampling node level. SOM mapping to each sampling node clearly shows riffle sediment surface texture change was spatially organized over the 11 sediment‐mobilizing events. Observations of pool sediment storage indicate that the pool‐riffle pair exhibited a coupled sedimentation response (i.e., similar texture trends between pool and riffle) following the 20‐year flood. The coupled response was characterized by a trend toward overall sedimentation conditions that were similar to those measured at the beginning of the study. The reported texture trends may be of interest to salmonid habitat studies that examine factors contributing to successful versus unsuccessful fry emergence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".