Background to flood control measures in the Red and Assiniboine River Basins
Bibliographic record
Abstract
The City of Winnipeg and southern Manitoba have a long history of flooding, with flood events being recorded soon after the region was settled in the early nineteenth century. A devastating flood on the Red River in 1950 resulted in some of the earliest benefit–cost analyses in Canada with respect to flooding, and justified the construction of major flood mitigation projects on the Red and Assiniboine Rivers in the 1960s and 1970s. These projects were primarily designed to reduce the risk to the City of Winnipeg. Other projects have been constructed outside of the Winnipeg area, which have reduced flood damages to towns, individual farmsteads and rural residences. The level of flood protection has been re-evaluated every time a new flood of record occurs, and this has resulted in significant upgrading of existing works and the addition of more communities with permanent flood protection. As a result of the flood protection system that has been developed in Manitoba over the last 60 years, the damage caused by floods has been significantly reduced over natural conditions. The purpose of this paper is to provide context to flooding in Manitoba with a consideration of how flooding, flood damage and the impact on citizens of Manitoba have been mitigated by permanent flood protection works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".