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Record W1988163613 · doi:10.1080/14786430500367347

Dynamic dislocation–defect analysis

2006· article· en· W1988163613 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of Canada
KeywordsFlow stressBurgers vectorWork hardeningMaterials scienceDislocationStrain rateMechanicsSlip (aerodynamics)Work (physics)Hardening (computing)PlasticityAluminiumThermodynamicsMathematicsPhysicsComposite materialMicrostructure

Abstract

fetched live from OpenAlex

The dynamic internal variables which control plastic flow can only be assessed by dynamic materials testing at any given instance. The testing method championned by our studies has been precision strain rate sensitivity (PSRS) whereby the change in flow stress due to a set change in strain rate is taken to be an operational measure of the activation volume and its product with the flow stress gives rise to the operational activation work. Also, from the work hardening slope, a modelled parameter proportional to the mean slip distance (λ) is simultaneously determined. The deviation from the linear Cottrell–Stokes relation as determined with the Haasen plot indicates the evolution of secondary defects other than monopole dislocations. Hence PSRS can assess the theoretical predictions of the activation distance (d) and work as a function of temperature, resulting in quantitative values that are in accord with dislocation theory at temperatures below that where point defects become mobile. A method to calibrate λ using Stage II slope θII shows that λ/ℓ, where ℓ is the mean forest dislocation spacing, is inversely proportional to θ, the work hardening coefficient. This analysis has led to a new plot of θII/θ versus b 2λ/ν where b is the Burgers vector and its slope is directly proportional to d. An example using an alumina-dispersed high conductivity copper shows that geometrically necessary punched out loops are continuously generated. The role of point defect mobility is dramatically illustrated by load drops in [001] aluminium crystals with the formation of slip clusters.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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