Under the Influence of Large Woody Debris: A Survey of the La Crosse River\nIn the Upper Midwest Driftless Area
Bibliographic record
Abstract
Streams are dynamic environments driven by the force of gravity and shaped by local climate, geology, and vegetation. Large woody debris (LWD) can have important influences on stream processes. The main influence of LWD on these systems is a resistance to flow; this added roughness induces a multitude of channel adjustments. Despite the importance of LWD, streams have been heavily managed by humankind, often involving the removal of debris to improve flow. Recent studies have highlighted the significance of large woody debris in mountain streams, particularly in the Pacific Northwest of the United States and Canada. However, there has been little research on the influence of LWD on streams in the Upper Midwest. This study will specifically investigate a stream (the La Crosse River) in southwestern Wisconsin’s Driftless Area. This area remained untouched by glaciers during the Last Glacial Maximum, but outwash from melting glaciers was deposited here, making the main bed material coarse sand. Combining stream survey methods (channel cross-sections) and a wood census, the influence of LWD was determined through statistical analysis of measurements of stream (velocity, depth, and width) and LWD (total counts, length, DBH, and volume) characteristics, in conjunction with qualitative analysis of detailed cross-sections. LWD are present in the study reach, but few relationships proved statistically significant, while local influences (initiation of scour and deposition) are clearly seen. Explanations of human, regional, historical, and bed form influences are explored.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".