Recalibration of partial load factors in the Canadian offshore structures standard CAN/CSA-S471
Bibliographic record
Abstract
The present paper describes a recalibration of the loading side of all the design check equations in the Canadian offshore structures standard CAN/CSA-S471, General requirements, design criteria, the environment, and loads (offshore structures). The recalibration was prompted by concerns about changing or improved load–load effect models and new load types and by Canada's intention to harmonize its offshore standards with the new International Organization for Standardization (ISO) offshore codes in the near future. Calibration is performed over wide ranges of combinations consistent with the normal application scope of CAN/CSA-S471. Updated load models are based on a more refined zonation of operational loads into loads of short duration and slowly varying live loads. Frequent environmental load processes and operational loads are modeled using Ferry-Borges–Castanheta pulse load models and infrequent environmental events and are based on point process models. The calibration is performed using a nonlinear optimization of an upwardly restrained safety objective function to result in optimal load factors, companion and combination factors, and optimal specified exceedance probabilities for infrequent load processes.Key words: load combinations, code calibration, pulse load models, safety factors, reliability levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".