Bibliographic record
Abstract
Abstract Traditional casing connection qualification programs provide confidence in performance, but at a high price. Manufacturer driven evaluations can be overly conservative in order to minimize development costs and allow the results to be applied to a wide range of applications. Evaluations by operators, either independent or in collaboration with manufacturers, can be customized to achieve the minimum amount of risk possible unique to design. Either option may limit the application of the data and can be costly to supplement. Utilizing prior data to compliment an evaluation will reduce scope, minimizing time and cost, but little formal guidance exists as to how to do this. Workgroups, such as the Product Line Evaluation (PLE) for Thermal Well Connections have created extensive guidance as to how to apply previous work to connection families, yet this remains confidential to the work group. Standards, such as ISO/PAS 12835 formally address the use of previous data in an evaluation, but do not offer guidance as to how to apply it. EVRAZ NA has experience working with clients to develop programs that combine existing data with benchtop/FEA based testing to minimize cost and risk. In this paper, examples of these evaluations will be discussed with the techniques used. This will demonstrate how confidence was built while not only saving time, but critically, cost to achieve an acceptable level of risk.
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".