High-resolution spatial and temporal sensitivity of river hydrodynamics: Implications for walleye (Sander vitreus) and lake sturgeon (Acipenser fulvescens) spawning habitat use in a large regulated river
Bibliographic record
Abstract
A reach-scale, high-resolution investigation of the spatial and temporal characteristics and sensitivities of hydrodynamic metrics on a large, low-relief, hydroelectrically regulated river in northwestern Ontario was performed. Velocity profile transects using an acoustic Doppler current profiler (ADCP) and the moving-vessel method were obtained at 7 – 10 discrete discharge levels at 47 cross-section locations over a 21 km reach, for 427 transects in total. Eighty-three (83) commonly computed hydrodynamic metric expressions of 23 hydraulic parameters were computed for each transect, addressing channel geometry, velocity, and flow complexity. \n \nSpearman correlations identified inter-dependencies between metric values, eliminating 61 parameters and resulting in a subset of 22 statistically independent and representative metrics. Visual and statistical examination of metric values revealed no consistent trend as a function of International Falls Dam (IFD) discharge. Uniform and featureless cross sections demonstrated low metric normalized variances indicating that channel homogeneity will result in aggregate hydraulic condition consistency and insensitivity. A smaller collection of cross sections possessing observable channel complexities producing large-scale turbulent structures displayed higher. Therefore, areas susceptible to macro-scale turbulence due to large roughness elements and complex morphology are likely to produce a broad range of hydraulic conditions under single-transect ADCP surveys. \n \nResults of the hydrodynamic characterization were subsequently cross-examined with coupled walleye (Sander vitreus) and lake sturgeon (Acipenser fulvescens) spawning survey findings to investigate the role of spatial and temporal habitat heterogeneity in spawning site selection. Cross sections were categorized according to spawning utilization (high, moderate, low). Statistical analysis identified a higher fraction of spatial heterogeneity metrics within those demonstrating significant differences between spawning and non-significant spawning locations. The observed preference for habitat diversity in spawning site selection corresponds to locations possessing large roughness elements and macroturbulence. The identified eco-hydraulic linkages will be applied to optimize spawning habitat for walleye and lake sturgeon on the Rainy River.
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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".