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Record W4407868937 · doi:10.5194/gmd-2024-175-rc1

Comment on gmd-2024-175

2025· peer-review· en· W4407868937 on OpenAlexaff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsDefence Research and Development CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsStatisticsEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

Abstract. We document the glacial system model (GSM), which includes a 3D thermo-mechanically coupled glaciological ice sheet model. The GSM is designed for large ensemble modelling in glacial cycle contexts. A distinguishing feature is the extent to which it addresses relevant uncertainties. The GSM has evolved from 2 and a half decades of effort to constrain last glacial cycle evolution of each major ice sheet. The core ice dynamics uses a hybrid shallow-shelf and shallow-ice approximation. It also includes one of the largest range of relevant processes for this context of any model to date, ranging from visco-elastic glacial isostatic adjustment with 0-order geoidal deflection to state-of-the-art subglacial sediment production, transport, and deposition. Other relevant distinguishing features include: permafrost resolving bed-thermodynamics, a fast diagnostic solution of down-slope surface drainage and lake filling, subgrid hypsometric surface mass balance and ice flow, simple thermodynamic lake and sea ice representations, subglacial hydrology with dynamically evolving partitioning between distributed and channelized flow, and surface melt that physically accounts for insolation changes via a novel insolation above freezing scheme. To address the most challenging part of paleo ice sheet modelling, the GSM includes both a 2D energy balance climate model and variants of traditional glacial indexed interpolation of fields from General Circulation Model (GCM) simulations, all under ensemble parametric specification. It also includes options for one and two way scripted coupling with climate models. We demonstrate the significant errors that can ensue in the glacial cycle simulation of single ice sheet when three aspects of glacial isostatic adjustment are ignored (as is typical). These are geoidal deformation, global ice load input, and correction of initial topography for present-day isostatic disequilibrium. We also draw attention to the relatively high sensitivity of the GSM (and presumably other ice sheet models) to the specification of the temperature dependence for basal sliding activation. The associated code archive includes configuration options for all major last glacial cycle ice sheets as well as idealized geometries and validation test setups.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.346
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0190.010
Insufficient payload (model declined to judge)0.3460.309

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 head0.023
GPT teacher head0.364
Teacher spread0.341 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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