The need for paleoflood investigations on the American reach of the Red River of the North
Bibliographic record
Abstract
Over the past century, the Red River of the North has been the least stationary river in the continental United States. In Canada, historical and paleoenvironmental evidence indicates severe floods were common during the early 1800s, with the record ce 1826 flood having an estimated peak discharge 50% higher than the second-most severe flood ever observed. Unfortunately, the recorded history of flooding upstream in the United States does not begin until seven decades after this event. If 1826 was an equally exceptional flood on American reach of the river, then current flood-frequency curves for the river underestimate significantly the risks posed by future flooding. Alternatively, if the American stretch did not produce a major flood in 1826, then the recent spate of flooding that has occurred over the past two decades is exceptional within the context of the past 200 years. Communities in the Fargo-Moorhead metropolitan area are building a 58-km long, $2.75 billion (USD) diversion channel that would redirect floodwaters westward around the two cities before returning it to the main channel. Because this and other infrastructure in North Dakota and Minnesota is intended to provide protection against low-probability, high-magnitude floods, new paleoflood investigations in the region would help local, state, and federal policy-makers better understand the true flood threats posed by the Red River of the North.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".