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Record W2476212315

Monitoring Ice Break-Up on the Mackenzie River Using Remote Sensing

2015· dissertation· en· W2476212315 on OpenAlexaboutno aff
P. Muhammad

Bibliographic record

VenueUWSpace (University of Waterloo) · 2015
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingGeologyEnvironmental scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

The Mackenzie Basin is composed of eight sub-basins (North Mountains, Liard, Peace, Athabasca, Great Bear Low Plains, Great Slave and Arctic Red) and includes three large lakes (Great Bear Lake, Great Slave Lake, Lake Athabasca) and three deltas (Peace-Athabasca Delta, Slave Delta, Mackenzie Delta), one of which is the world’s largest inland delta (Peace-Athabasca Delta). Annually, the Mackenzie River experiences freeze-up during the fall season and ice break-up in the spring, having an important influence on the basin hydrology Furthermore, the type of ice break-up event is dependent on the magnitude of hydrological and meteorological conditions present. In light of the decreasing network of ground-based stations operated by the Water Survey of Canada on the Mackenzie River, this study explored the use of satellite remote sensing data to improve monitoring capabilities during the ice break-up period. \n \nMODIS Level 3 snow products (MOD/MYD10A1) and MODIS Level 1B radiance products (MOD/MYD02QKM) are used to monitor ice cover during the break-up period on the Mackenzie River, Canada, for 13 ice seasons (2001-2013). The initiation of the break-up period was observed to occur between days of year (DOY) 115-125 and end DOY 145-155, resulting in average melt durations of 30-40 days. Floating ice running northbound could therefore generate multiple periods of ice-on and ice-off observations at the same geographic location. At the headwaters of the Mackenzie River, ice break-up was thermodynamically driven as opposed to dynamically, as observed downstream near the Mackenzie Delta. MODIS observations also revealed that ice runs were largely influenced by channel morphology (islands and bars, confluences and channel constriction). MODIS was found to be a powerful tool for monitoring ice break-up processes at multiple geographical locations simultaneously along the Mackenzie River. Finally, MODIS was found to be a viable tool for estimating river ice velocity where channel morphology least affected river flow. Ice run velocities north of 66° N ranged from 1.21-1.84 ms-1.

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)0.000
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 score0.696
Threshold uncertainty score0.605

Distilled classifier scores by category (both heads)

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

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 head0.037
GPT teacher head0.224
Teacher spread0.187 · 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
Published2015
Admission routes1
Has abstractyes

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