The 2014 flood event in the Assiniboine River Basin: Causes, assessment and damage
Bibliographic record
Abstract
The 2014 Assiniboine River flood caused millions of dollars in damage to southern Manitoba and Saskatchewan. Heavy precipitation in late June and early July resulted in states of emergency being declared in both provinces as water levels reached record levels in the Upper Assiniboine Watershed, and near record levels downstream of the City of Brandon. In spite of the significant measures both provinces took to protect their citizens, the flood washed out roads and bridges and caused severe damage to properties, riverbanks and dikes. Some communities became isolated due to the flood damage to roads. Several hundred people were evacuated from homes and communities in Manitoba and Saskatchewan. The other major impact was on the farming community. The timing of this flood made it more costly than the 2011 flood from the farming perspective since most farmers had already planted their crops. The origin of the 2014 flood was different from any other major flood of the Assiniboine River since records have been kept; this was a true summer flood in that the spring freshet had already passed and flows had receded to a baseflow condition. The event was entirely due to rainfall on a basin where conditions were extremely wet and had been since before the 2011 flood. This resulted in record flows being recorded on many tributaries, and flows nearly equal to those of the 2011 event along the Assiniboine River. It should be noted that the 2011 event was initially a snowmelt event due to greater-than-normal accumulation of winter snowpack. The 2014 summer flood was “unprecedented” in the previous 130 years of observations of the Assiniboine River flows and follows only three years after the 2011 event that was described with the same adjective. The influence of anthropogenic activities, both in changing the land use and extending the drainage network, undoubtedly has played a significant role in altering prairie hydrology and potentially increasing the magnitude of flood events.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".