Natural and anthropogenic controls on sediment rating curves in northern California coastal watersheds
Bibliographic record
Abstract
Abstract The watersheds along the north coast of California span a wide range of geologic settings, tectonic uplift rates, and historic timber harvest activity. Known trends in how each of these factors influence erosion rates provides an opportunity to examine their relative importance. We analyzed 71 watersheds within nine larger river basins, investigated the factors influencing suspended sediment rating curves (SRCs), investigated how SRCs varied among our study watersheds, and used Random Forest modeling (RFM) to determine which environmental characteristics and land management metrics influence SRC shapes, vertical offsets, and slopes. While SRCs typically take the form of a power function, they also can exhibit threshold or peak relationships. First, we found both power and threshold relationships for the SRCs within our study watersheds. Second, the SRC offsets and slopes systematically varied with regional tectonic uplift. Third, SRC offsets increased in several watersheds following intensive timber harvest events and SRC slopes decreased due to a greater relative increase in suspended sediment concentration at lower flows than higher flows. Our RFM correctly classified 96% of the SRC shapes using two near‐channel metrics; near‐channel precipitation‐sensitive deep‐seated landslide susceptibility and near‐channel soil erodibility. Our RFM models also showed that timber harvest activity and near‐channel local relief can explain 40% of the variability in SRC offsets, whereas tectonic uplift rates, millennial‐scale erosion rates, and precipitation patterns explain 40% of the variability in SRC slopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".