Drainage Challenges in the Whapmagoostui First Nation Community: A Case Study
Bibliographic record
Abstract
The Cree Community of Whapmagoostui is facing serious runoff water management issues on its territory. During periods of heavy rain and during the snow-melting season, significant accumulation of runoff water in certain sectors of the community impedes pedestrian and vehicular traffic, in addition to causing damage to streets and buildings. Traffic areas become difficult and frequent interventions (levelling and surfacing) are required. Repeated levelling operations caused aggregate segregation and exacerbated the emergence of surface holes and depression. This situation worsened over the recent years as the effects of climate changes become more a reality. CIMA+ proposed and tested in 2016–2017 a simple concept using conventional detention plastic chambers to achieve water storage, at a first location in the elementary school yard. These chambers allow for water percolation but also for sand and particles sedimentation. After two years of service performance in the field, the system has proven efficient and has provided a practical solution adapted to the needs of the community. Due to the remoteness of the Whapmagoostui Community which has no terrestrial link to other southern communities, supplies and construction materials can only be sea or air lifted. The lightweight plastic detention chambers become a preferred component of the drainage system, relatively easily transported, stored, and put in place. One of the key elements of the success of this project is the implication of a local Cree workers crew that has been assembled by the public works department. The Cree workers and CIMA+ team learned to work together, to plan the work sequence, to secure the materials and equipment ahead of time, to prevent health and safety hazards, and to measure the importance of quality craftsmanship. The CIMA+ team will take a leadership role in the community as the construction work activities intensify in the near future.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".