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Record W7010572207

The Influence of Site Conditions and Surface Vegetation on Snow Accumulation and Ablation in the Elk Valley, British Columbia, Canada

2023· dissertation· en· W7010572207 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowSnowmeltHydrology (agriculture)Vegetation (pathology)Elevation (ballistics)PrecipitationOverburdenSnow field
DOInot available

Abstract

fetched live from OpenAlex

Surface mining of coal in the Elk Valley, British Columbia involves the blasting of overburden rock to access the underlying coal formations. Waste rock is placed in adjacent valleys, altering the dynamics of the hydrological process within the watershed. As part of a multi-year R&D program examining the impacts of surface mining on watershed hydrology in the Elk Valley, British Columbia, this study investigates how surface vegetation atop waste rock influences snow accumulation and ablation, and the ability of a physically based model to simulate these hydrological processes. During the 2014 melt season, meteorological observations, eddy covariance turbulent fluxes and snow conditions were measured at three sites; 1) a bare waste-rock surface, 2) a waste-rock surface covered with agronomic grass species, and 3) a mixed pine stand on waste-rock. Variations of meteorological data, turbulent fluxes and measured snow conditions between sites were assessed. Elevation was the dominant control of snow accumulation, with the upper elevation site recording a maximum snow water equivalent of 67 cm, whereas the lower elevation site had a maximum snow water equivalent of 17 cm. Ablation was driven largely by incoming short-wave radiation, which at the bare waste rock and grass covered waste rock sites was greater than the forested site. Turbulent flux contributions to snow ablation were limited in the forested site relative to the bare waste rock and grass covered waste rock sites. The physically based Cold Regions Hydrological Model (CRHM) was able to effectively simulate the influence of vegetated waste-rock surfaces on the hydrological system. However, model parameters regarding vegetation cover and blowing snow required careful calibration to obtain a suitable model output. Results of this study can be used to more accurately model the influence of vegetated waste-rock on the timing and magnitude of the spring freshet in the Elk Valley, British Columbia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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