Impacts of urbanization on West Nose Creek: a Canadian experience
Bibliographic record
Abstract
The lower reaches of West Nose Creek have been subject to urbanization since the 1970s, leading to channel widening and excessive erosion. This paper discusses what would likely happen if urbanization were allowed to continue in the same manner. Comparisons are presented of the channel width and depth for both the upstream rural and downstream urbanizing reaches. Estimates of the evolution of the creek were generated by linking the dominant discharge to the entire shape and volume of the hydrograph that the creek is subjected to rather than solely considering peak discharges. otential remedial measures and stormwater management philosophies are discussed in relationship to instream flow needs (IFNs) initiatives. IFNs are generally developed by relating the amount of suitable aquatic habitat to the quantity of flow. The emphasis has so far been on IFNs for large river systems. Unfortunately, none of the IFN approaches cover streams that are subject to significant urbanization. In urbanized streams the issue is not as much the impacts due to withdrawals but due to significantly increased runoff rates and volumes generated within the urban areas. Examples are provided how fisheries habitat is impacted by the changed hydrologic regime and changed stream morphology.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".