The 11th SPE Comparative Solution Project: Submitted Data
Bibliographic record
Abstract
Data submitted to the 11th Society of Petroleum Engineers Comparative Solution Project. Contains the sparse and dense data files of 77 results submitted by 18 participating groups for three cases SPE11A-C. Each zip file contains one such result, where the name speX_NAMEY.zip indicates Result Y for Case SPE11X of Participant NAME. Unpacking a result file yields one sparse data file speX_time_series.csv, several dense data files speX_spatial_map_TIME.csv, and, optionally, performance data files. A sparse data file contains the evolution of several scalar quantities over time, while a dense data file contains the spatial distribution of several scalar quantities at a particular reporting time step. For more information, see the related publication. The results can be processed by the scripts provided in the repository github.com/Simulation-Benchmarks/11thSPE-CSP. From the repository's website, access to a Jupyter Hub is enabled that allows to run the scripts on the full dataset.
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 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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.398 | 0.388 |
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".