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Record W4321995690 · doi:10.5194/egusphere-egu23-11076

Physical processes driving 'switching events'

2023· preprint· en· W4321995690 on OpenAlexaff
Gabriela Clara Racz, Kevin Mirng En Yeo, Aly Thobani, Sean Henry, Maryam Zarrinderakht, Camilo Rada, Christian Schoof

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyElectrical conduitBoreholeGlacierPermeability (electromagnetism)CreepGeomorphologyGeotechnical engineeringMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Commonly, the parts of the glacier bed that are hydraulically connected to the surface experience significant diurnal variations in water pressure, in response to cycles of surface melting. Closely spaced points on the bed often exhibit nearly identical temporal variations in water pressure, suggesting that they are connected not only to the surface but to each other through conduits along the bed. This behaviour is typically observed directly through instrumented boreholes drilled to the glacier bed. A ‘switching event’ occurs when one of a pair of boreholes abruptly changes from being connected, in the sense of exhibiting the same diurnal oscillations as the other borehole, to being disconnected, or vice versa. A switching event is indicative of a connection through a subglacial conduit being closed, or opened, and therefore provides a limited but highly specific window into the evolution of subglacial conduits and permeability.However, in most subglacial drainage models, conduits are not represented individually but averaged over a small area of the bed to produce a macroporous continuum representation as a ‘water sheet’, quantified by a mean conduit depth h. The most common assumption is that the water sheet consists of linked cavities and that these open due to basal sliding over bed roughness, and close due to viscous creep (e.g. Hewitt, 2011). Within that framework, the simplest mechanism for a switching event is that a connection is established or closed when the sheet thickness h passes through some percolation threshold hc (Rada and Schoof, 2018).We want to test whether the observed switching events can be explained by that mechanism, which in turn implies that two conditions must be met: water sheet depth indeed evolves according to a competition between opening due to basal sliding and creep closure, and that a simple threshold in h suffices to capture the geometric complexity involved in creating or closing connections at the bed.In a large dataset of borehole water-pressure time series, we identify borehole pairs that exhibit strong evidence of switching behaviour. We assume that switching events can be described by the evolution of a water sheet, with connections between boreholes being opened and closed as sheet thickness passes through a threshold value as described above. We use the switching event catalogue we have created to invert for parameters in the sheet evolution model using a binary indicator function for connectedness to compute the model data mismatch in the absence of any other direct measures of sheet thickness.This procedure allows us to capture the majority of observed switching events with plausible parameter values. The exception is a set of short-lived periods of connectedness characterized by switching events that are clustered in space and time. In a complementary study (Racz et al, 2023 in prep.), we, therefore, investigate if this class of switching events can instead be explained by an alternative mechanism in which the sudden resumption of surface water supply, following a period of snow cover, drives the propagation of a hydrofracture (e.g. Tsai and Rice 2010, 2012).

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.256
Teacher spread0.243 · 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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