Progress Toward an Open-Source Drilling Community: Contributing and Curating Models
Bibliographic record
Abstract
Abstract As a follow-up to the challenge set forth by (Pastusek et al, 2019) to create an open-source drilling community for modelling and data, this paper presents the charter, contribution methods, workflows, and interoperability standards of the open source drillstring modelling community. A series of examples, ranging from simple drillstring and fluids models to coupled drillstring dynamics models are included. They demonstrate the coding styles, validation, and verification necessary to submit a model to the repository. These models include a torsional drillstring model, a coupled axial-torsional drillstring dynamics model with integrated control system responses, an advanced fluid model for drilling fluids, and a bottomhole assembly dynamics model. The drillstring modelling and overall optimization communities are invited to make use of these models and contribute their own to create an active ecosystem that promotes progress.
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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 | 0.143 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.042 |
| Open science | 0.013 | 0.037 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".