Geomorphic comparison of two Atlantic coastal rivers: Toward an understanding of physical controls on Atlantic salmon habitat
Bibliographic record
Abstract
Abstract River channel substrate size and mobility are important to Atlantic salmon spawning and rearing success. We compare morphology and bed sediment between two North American Atlantic coastal streams (Narraguagus River, Maine, USA and Jacquet River, New Brunswick, Canada). The watersheds have similar drainage areas and mean annual precipitation, but differing relief structure, channel longitudinal profiles and numbers of returning salmon. The lower‐relief Narraguagus River is segmented into steeper (gradient >0.002) and flatter reaches (gradient <0.0005). Flat reaches, including mainstem lakes, act as sediment sinks, preventing the continuity of downstream sediment transport. Based on field measurements, the Narraguagus River has a larger high‐flow width to depth ratio than the Jacquet River, but this difference is principally the result of outliers from low‐gradient channel reaches. Measurements of substrate grain size reveal finer river‐bed sediments on the Narraguagus River, however, Shields parameter calculations indicate that bed sediment should be mobile during high flows in both streams. We use the Shields equation to predict grain size based on channel slope, width and drainage area measured from digital elevation models (DEM) and aerial photographs. Predictions of median grain size agree with field measurements within a factor of 2 for 62–70% of the survey stations. We suggest ways that model misfits may provide opportunities to prioritize reach‐based restoration efforts. Based on expected grain size, we estimate 62% spawning and 68% rearing habitat along the length of the Narraguagus River, and 28 and 95% respectively on the Jacquet. Overall, glacial history and relief structure appear to be the first‐order controls on substrate grain size and habitat quality in these two rivers. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".