MétaCan
Menu
Back to cohort

Reconstructing ice force-displacement development in structural assessments of freshwater, polycrystalline ice impacts

2023· article· en· W4327922489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Impact Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsNational Research Council Canada
FundersSenter for Autonome Marine Operasjoner og SystemerNorges ForskningsrådNorges Teknisk-Naturvitenskapelige Universitet
KeywordsDisplacement (psychology)GeologyBrittlenessMechanicsGeotechnical engineeringMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

• Reconstructing realistic ice force-displacement development. • Model parameters should result in physically realistic crushing specific energy (CSE). • PA model in ISO 19906 Clause A.8.2.4.7.3 & .3.5 fit CSE ≈ 5.6 kJ/kg for a 10 −2 event. • PA model of IACS for PC1 and PC3 fit CSE of 14 kJ/kg and 3.0 kJ/kg, respectively. Using the ISO19906 local pressure-area relationship p = 7.4 A −0.7 as a process pressure-area curve to validate numerical ice models and reconstruct force-displacement development in damage assessment studies may lead to overestimation of the energy absorption capacity of ice found in nature. This is because the way this relationship is derived is different from how it is used in structural assessments of ice impacts. In the following paragraphs, we explain (with examples) how to reconstruct realistic ice force-displacement relationships in the absence of empirical force histories while focusing on freshwater, polycrystalline ice crushing, within the brittle regime, against a rigid structure.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.303
Teacher spread0.292 · 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