Bibliographic record
Abstract
A different type of floodingStorms soaked southern parts of Manitoba (in Central Canada) last Wednesday, leaving one small town flooded up to its knees.St-Pierre-Jolys, a community of about a thousand in the south-eastern part of the province, was hit with at least 75 mm of rain in one hour, according to Environment Canada.In the main part of town, water pooled up to one meter deep, forcing some to stay up late helping as the aging infrastructure struggled to handle the water.Mayor Mona Fallis said she was in a meeting when the skies opened up."We knew it was raining, and when we came out, all of the streets were flooded at least up to the curb," said Fallis.Though summer storms are common in the area, Fallis said this one was exceptional: "Never in my 50 plus years living in this community have I ever seen the water come up so high so quickly."Brandon, Manitoba, a city in the southwest part of the province, was also hit with about 60 mm of rain from the storm system that brought 100-kilometre-per-hour gusts and coin-sized hail.Summer storms in the area were never the main source of flooding.Nearby large prairie rivers, Red and Assiniboine, were creating a huge flood damage in 1886, 1950, 1997, 2011, 2013 as a consequence of typical snowmelt driven spring flooding.The residents of these communities are learning about the new reality and 'new type of flooding' .The rest of Canada seems to be exposed to a very similar changing conditions and pattern of flooding.The Insurance Bureau of Canada and the Institute for Catastrophic Loss Reduction in unpublished analyses (2014) are showing that direct insurance payments due to urban flooding have reached $2 billion annually.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.328 | 0.202 |
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".