Lateral Migration of the Red River, in the Vicinity of Grand Forks, North Dakota
Bibliographic record
Abstract
River channels are dynamic landforms that play an important role in understanding the alluvial changes occurring within this area. The evolution of the Red River of the North within the shallow alluvial valley was investigated within a 60 river mile area north and south of Grand Forks, North Dakota. Despite considerable research along the Red River of the North, near St. Jean Baptiste, Manitoba, little is known about the historical channel dynamics within the defined study area. A series of 31 measurements were taken using three separate methods to document the path of lateral channel migration along areas of this highly sinuous, low-gradient river. Specifically, historical aerial photographs, cross sectional elevation based models, and PLAT maps were used to determine how the river channel has laterally migrated over the past 142 years. Locations of the measurements were dependent upon availability of data within each specified method, as measurements were only taken from where movement could be seen and documented. Results from these methods indicate that the channel has undergone noticeable changes in some regions of the river. The maximum migration distance of this river found using cross sectional data is 1455.42 meters, and is seen by reoccurring oxbows and scars throughout the broad flat valley. Utilizing all three methods within the study area, it is found that rates are averaging between 0.01 and 0.38 meters/year. This shows a maximum distance of lateral migration of 54.4 meters over the span of 142 years, and implies that the low energy, mud-dominated river is undergoing long-term, lateral migration at a low rate. Overall, these results provide important context for assessing whether similar patterns and rates of channel migration exist throughout the Red River of the North. Viewing these low rates found throughout the study area shows no potential risk or harm to the city’s infrastructure within the next 100 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".