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GRAIN REFINEMENT OF PURE MAGNESIUM USING ROLLED ZIRMAX® MASTER ALLOY (MG-33.3ZR)

2003· article· en· W22379718 on OpenAlexaboutno aff
Ma Qian, David St John, M. T. Frost, Matthew Barnett

Bibliographic record

VenueMagnesium technology · 2003
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsZirconiumMaterials scienceIngotAlloyMetallurgyMagnesiumZirconium alloyMicrostructureRefining (metallurgy)Particle (ecology)Particle sizeChemical engineering

Abstract

fetched live from OpenAlex

This study analyses the relationships between patients' dispositional optimism and pessimism and the coping strategies they use. In addition, the coping strategies repercussions on adjustment to chronic pain were studied. Ninety-eight patients with heterogeneous chronic pain participated. The assessment tools were as follows: Life Orientation Test (LOT), the Vanderbilt Pain Management Inventory (VPMI), the McGill Pain Questionnaire (MPQ), Hospital Anxiety and Depression Scale (HADS), and the Impairment and Functioning Inventory for Chronic Pain Patients (IFI). The hypothetical model establishes positive relationships between optimism and the use of active coping strategies, whereas pessimism is related to the use of passive coping. Active coping is associated with low levels of pain, anxiety, depression and impairment and high levels of functioning. However, passive coping is related to high levels of pain, anxiety, depression and impairment and low levels of functioning. The hypothetical model was empirically tested using the LISREL 8.20 software package and the unweighted least squares method. The results support the hypotheses formulated regarding the relations among optimism, pessimism, coping and adjust of chronic pain patients. By analysing optimism among chronic pain patients, clinicians could make better predictions regarding coping and adjustment.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations10
Published2003
Admission routes1
Has abstractyes

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