MétaCan
Menu
Back to cohort
Record W4210867846 · doi:10.1002/esp.5339

Time‐lapse photogrammetry reveals hydrological controls of fine‐scale High‐Arctic glacier surface roughness evolution

2022· article· en· W4210867846 on OpenAlexaff
Tristram Irvine‐Fynn, Tom Holt, T. D. James, Mark W. Smith, Nick Rutter, Philip R. Porter, Andy Hodson

Bibliographic record

VenueEarth Surface Processes and Landforms · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsQueen's University
FundersRoyal Geographical SocietyLeverhulme Trust
KeywordsGeologyGlacierSnowGlacier mass balanceSurface roughnessArcticElevation (ballistics)ClimatologyPhysical geographyGeomorphologyAtmospheric sciencesGeographyOceanographyGeometry

Abstract

fetched live from OpenAlex

Abstract In a warming Arctic, as glacier snowlines rise, short‐ to medium‐term increases in seasonal bare‐ice extent are forecast for the next few decades. These changes will enhance the importance of turbulent energy fluxes for surface ablation and glacier mass balance. Turbulent energy exchanges at the ice surface are conditioned by its topography, or roughness, which has been hypothesized to be controlled by supraglacial hydrology at the glacier scale. However, current understanding of the dynamics in surface topography, and the role of drainage development, remains incomplete, particularly for the transition between seasonal snow cover and well‐developed, weathered bare‐ice. Using time‐lapse photogrammetry, we report a daily timeseries of fine (millimetre)‐scale supraglacial topography at a 2 m2 plot on the Lower Foxfonna glacier, Svalbard, over two 9‐day periods in 2011. We show traditional kernel‐based morphometric descriptions of roughness were ineffective in describing temporal change, but indicated fine‐scale albedo feedbacks at depths of ~60 mm contributed to conditioning surface topography. We found profile‐based and two‐dimensional estimates of roughness revealed temporal change, and the aerodynamic roughness parameter, z0, showed a 22–32% decrease from ~1 mm following the exposure of bare‐ice, and a subsequent 72–77% increase. Using geostatistical techniques, we identified ‘hole effect’ properties in the surface elevation semivariograms, and demonstrated that hydrological drivers control the plot‐scale topography: degradation of superimposed ice reduces roughness while the inception of braided rills initiates a subsequent development and amplification of topography. Our study presents an analytical framework for future studies that interrogate the coupling between ice surface roughness and hydro‐meteorological variables and seek to improve parameterizations of topographically evolving bare‐ice areas.

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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

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

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEarth Surface Processes and LandformsSame topicCryospheric studies and observationsFrench-language works237,207