Measuring geomorphology in river assessment procedures 1: A global overview of current practices
Bibliographic record
Abstract
Abstract Despite geomorphic processes being increasingly recognized as a vital component of river management projects, evidence suggests that they may not be adequately captured in common river assessment procedures. We reviewed 91 river assessment procedures from around the world to evaluate their effectiveness in capturing geomorphic processes relevant for river management goals. Our objectives were to summarize which common geomorphic indicators are measured and how in different types of river assessments categorized based on their main focus: geomorphic, physical habitat, mixed geomorphic and habitat, and hydromorphology. Our analysis identified differences in the types of geomorphic indicators included and measurement methodologies between the types of assessment procedures. Some geomorphic processes, such as sediment transport, are nearly completely absent from all assessments, despite their importance for geomorphic processes. The variability among assessment procedures suggests that a single procedure is unlikely to capture all geomorphic components required to support every river management programs. Here, we discuss how the strengths and limitations of different assessment types can be used to guide decisions around how to select assessments and geomorphic indicators to support management project goals. A companion paper expands the discussion of how to plan effective river assessment procedures to support unique management goals.
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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 | 0.131 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".