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Record W3097032684 · doi:10.11575/prism/26960

Modelling Palaeohydrological Controls in Postglacial Mountain Drainage Basins

2017· article· en· W3097032684 on OpenAlexaboutno aff
Peter Klassen

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyDrainageDrainage basinHydrology (agriculture)Physical geographyEnvironmental scienceGeomorphologyGeographyCartographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Quaternary glacial processes were a driving factor in the formation of the landscape of the Canadian Rocky Mountains. Modelling of basin scale hydrology in glaciated mountain regions, including the associated morphology and sedimentology, has not previously been undertaken. The present study investigates drainage basin hydrology for the Kananaskis Valley immediately after glaciation. Low order drainage basins are analyzed to define a prototypical basin to facilitate groundwater modelling. The surface and groundwater modelling software HydroGeoSphere is used to model the prototypical drainage basin hydrology. Results show that glaciated mountain valley morphology and glacigenic sediments are pertinent controls on both the surface and groundwater dynamics in low order drainage basins. Sediments with high hydraulic conductivity were found to play the most significant role for the fate of precipitation in mountain drainage basins. Understanding how a history of glaciation affects mountain drainage basins is crucial for fully conceptualizing the hydrology of these regions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.217
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2017
Admission routes1
Has abstractyes

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