Evaluation of Tensile Strength of ACSR Conductors Based on Test Data for Individual Strands
Bibliographic record
Abstract
A probabilistic, numerical method for evaluating tensile properties of aluminum conductors, steel reinforced (ACSR) is developed. Analysis of data from routine-production tests performed on individual aluminum and steel strands enabled establishing the shape of the distribution, degree of truncation, and statistical parameters of the diameter, breaking force, and breaking stress. Modeling the mechanical behavior of individual strands allowed to investigate the tensile properties of completed ACSR: to estimate the parameters of the breaking tensile force and characteristic strength with a given exclusion limit; to study the convergence of the simulated empirical distribution toward the theoretical normal distribution; and to establish a correlation between the breaking tensile stress and the ratio of aluminum-to-steel cross-sectional area. Results from numerical simulations show concordance with the IEC and ASTM standard specifications
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".