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Record W2759102728

Alluvial Floodplain Classification and Organization in Low-relief Glacially Conditioned River Catchments

2014· dissertation· en· W2759102728 on OpenAlexfundaboutno aff
Roger T.J. Phillips

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersCentre for Global Change Science, University of TorontoNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsFloodplainAlluviumAlluvial plainGeologyHydrology (agriculture)GeographyGeomorphologyCartographyPaleontologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

The imprint of late Pleistocene glaciation on river systems is an essential theme in Canadian geomorphology. Existing ideas about glacial legacy effects tend to focus on mountainous environments, which are different from the low-relief physiography of the Laurentian Great Lakes region. This study investigates river landforms in southern Ontario to develop an improved conceptual fluvial landscape model, reflecting glacial legacy and post-glacial fluvial processes. The analysis is based on an original dataset, including basic channel observations from over 500 field sites, alluvial floodplain properties from 109 sites, and published physiographic mapping from digital sources.Glacial signatures are evident in river profiles extracted from a digital elevation model (DEM) for 22 river catchments in southern Ontario. Stream power and slope-area analysis stratify river slopes by glacial landform types and demonstrate significant differences between rivers incised into glacial moraines versus plains. A stream length-gradient index provides a relative measure of how river profiles are oversteepened or understeepened by glacial landforms relative to a theoretical graded profile.Four first-order alluvial floodplain classifications are presented using k-means clustering analysis. Predictive variables are explored using PCA and discriminant analysis, producing two principal components: (1) stream power-resistance and (2) floodplain sedimentology (or floodplain sand equivalent,

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.009
GPT teacher head0.206
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2014
Admission routes2
Has abstractyes

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