Transport and settlement of organic matter in small streams
Bibliographic record
Abstract
Summary 1. After it enters streams, terrestrially derived organic matter (OM) rapidly absorbs water. Using field and laboratory experiments, we examined how this process affected the buoyancy, settling velocity, transport distance and retention locations of four types of organic matter typically found in Pacific coastal streams (‘flexible’ red alder leaves and three ‘stiff’ particle types – Douglas‐fir needles, red cedar fronds and Douglas‐fir branch pieces). 2. Immersion in water rapidly changed the physical characteristics of alder leaves, Douglas‐fir needles and red cedar fronds, which all reached constant still‐water settling velocities after only a few days of soaking. In contrast, the settling velocity of branch pieces continued to increase for 13 days, eventually reaching much higher values than any other OM type. Dried alder leaves became negatively buoyant after only two days of immersion, while other types took substantially longer (up to 24 days) before the specific gravity of all particles was >1. 3. We released saturated OM particles in an experimental channel and found that all particle types travelled further in a fast, shallow ‘riffle’ than a slow, deep ‘pool’. Comparisons with a passive settlement null model indicated that leaves were retained more rapidly than expected in the riffle (by large protruding stones), while the three stiff particle types travelled further than expected (probably due to turbulent suspension) and were retained when they settled in deeper water between larger stones. In pools, passive settlement appeared to dominate the retention of all OM types, with leaves travelling furthest. 4. These retention patterns corresponded well with those observed when saturated OM particles collected in the field were released in two pools and two riffles in a second‐order coastal stream. 5. When the experimental channel and in‐stream data were combined, the retention rates of the three stiff OM types were closely related to calculated Rouse numbers (Rouse number = particle settling velocity/shear velocity), whereas the retention rate of alder leaves was not. This suggests that different physical mechanisms are responsible for the retention of leaves and stiff OM types in shallow streams.
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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) | 0.007 | 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".