Does variable channel morphology lead to dynamic salmon habitat?
Bibliographic record
Abstract
Abstract Stream channel morphology forms the template upon which hydraulic aspects of aquatic habitat are created, yet spatial and temporal variability in habitat imposed by changing morphology is not well understood. This paper presents a conceptual model linking sediment supply patterns to spatial and temporal variability in channel form and aquatic habitat. To evaluate this model, change over time in three habitat variables is quantified using a 2D hydrodynamic modeling approach. A 45‐year record of topographic data from Carnation Creek, a catchment in coastal British Columbia, is used for the flow modeling. Using the Nays2DH modeling platform, water depths and velocities are simulated in eight channel segments located at different positions relative to locations of historical colluvial input using seven flow levels ranging from 3% to 400% of mean annual discharge (0.02 to 3.31 m s ). Results indicate that habitat availability changes through time as a result of sediment supply‐driven changes to channel morphology and wood loads, but patterns in habitat vary as a function of dominant channel segment morphology. Spatial and temporal variability in morphology also influences the relationship between habitat availability and river discharge, leading to non‐stationary habitat‐discharge rating curves. When habitat areas are predicted by applying these curves to daily flow series spanning annual dry seasons, over 50% of the variance in cumulative seasonal habitat area can be explained by year‐to‐year changes in channel morphology and wood loading, indicating that changing morphology is an important factor for driving temporal habitat variability. This variance is related to the morphological variability of a channel segment, which in turn is associated with the segment position relative to zones of colluvial input. Collectively, these results suggest that variability in habitat is impacted by channel morphology, and can be evaluated partly on the basis of a channel's sediment supply regime. © 2019 John Wiley & Sons, Ltd.
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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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".