Reframing and operationalizing holistic, geomorphologically informed river management in British Columbia, Canada
Bibliographic record
Abstract
As watershed geomorphology acts as a physical template upon which a range of biotic and abiotic processes operate, a thorough geomorphic understanding of watersheds provides a key foundation of knowledge upon which a full range of environmental management decisions can be developed. While British Columbia (BC) is home to a long history of study and notable advances in the science of geomorphology and related fields, this knowledge has not been incorporated into environmental management or planning approaches to its full potential. We argue that a geomorphologically informed approach to river management needs to be developed and adopted to provide a foundation for holistic, coherent, integrative, proactive, sustainable, and cost-effective solutions to a range of management challenges. The perspectives presented in this paper originate from a 3-day River Styles Framework training workshop that was attended by Indigenous and non-Indigenous experts in geomorphology, ecology, hydrology, and restoration in September 2024 in Campbell River, Vancouver Island (the traditional territory of the Liǧ w iłdax̌ w people). The motivation of this work is to give a voice to the concerns of industry practitioners, allowing them to express their views on the good things that are happening in-practice, the impediments to advancing practice, and their thoughts on what needs to happen to move forward. We end by outlining how the River Styles Framework, a widely adopted approach to geomorphic characterization and interpretation of riverscapes, could be used to operationalize a geomorphologically informed approach to river management by organizing, building out, and synthesizing watershed knowledges and programs in BC.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".