Alluvial floodplain classification by multivariate clustering and discriminant analysis for low‐relief glacially conditioned river catchments
Bibliographic record
Abstract
Abstract River classifications provide useful frameworks to understand complex fluvial landscapes and to manage freshwater ecosystems. Alluvial floodplains for rivers in low‐relief glacially conditioned catchments of southern Ontario (Canada) are classified and tested using a sequence of multivariate statistical analyses. An original dataset of 109 floodplain sites is investigated using k‐means clustering, principal component analysis, and discriminant analysis statistical approaches. Four primary floodplain types are proposed representing basic morphological, stratigraphical, and sedimentological characteristics. Classifications are successfully discriminated by two principal dimensions: (1) stream power‐resistance; and (2) floodplain sedimentology. The latter is most efficiently represented by the availability of alluvial sand, and specifically a new variable defined as floodplain sand equivalent (FSE). Floodplain types are generally consistent with previous river classifications, however the glacial legacy requires refined classifications which account for inherited cobble bed materials and patterns of sand supply. Representing the residual variability of stream power‐resistance correlations, a third explanatory dimension of sediment transport is suggested, and may explain some within‐class variability in channel morphology. Balancing the opposing concepts of fluvial process domains and landform continuums, the potential for transitional floodplain types is also explored. The proposed first‐order alluvial floodplain classifications provide a basis from which to further investigate geomorphological diversity within the context of complex glacial legacy effects in low‐relief settings. Future research to reveal the spatial arrangement and linkages of distinct morphological groups within a regional landscape mosaic is expected to provide insights into patterns of post‐glacial fluvial adjustment. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".