Step-pool and cascade morphology, Mosquito Creek, British Columbia: a test of four analytical techniques
Bibliographic record
Abstract
The identification and geometric definition of individual cascade and step-pool bedforms are investigated in a steep, coarse-grained, mountain stream, Mosquito Creek, by testing four analytical techniques: visual identification, zero-crossing, bedform differencing, and power spectral analysis. The test is the first use of these techniques in a headwater stream, and the analysis of two bed profiles showed that visual identification was able to (i) identify, (ii) determine the geometry of, and (iii) classify the type of individual bedforms better than the other methods. The other techniques were not able to differentiate step-pools from cascades, and the large range of grain sizes and bedform heights hampered their ability to consistently identify stepped bedforms. The step-pool (pronounced, channel-spanning steps that alternate with channel-spanning pools) and cascade (multi-tiered, partially channel-spanning structures) morphology in Mosquito Creek has formed in the last 20 years as fluvial action has restructured its previously engineered, revetment-lined, planar bed. The channel bed exhibits a morphologic regularity that power spectral analysis captured as periodic fluctuations in the bed profiles, with mean wavelengths slightly greater than those identified by the other methods. Further, the active reorganization of revetment has formed stepped structures with geometries similar (i.e., height to wavelength ratios) to stepped features found in natural mountain streams. Channel slope partially controlled bedform geometry (wavelength and height), and bedform height weakly controlled individual step spacing, but there was no relation between wavelength and grain size (D90).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".